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   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 2\n",
    "------------\n",
    "\n",
    "Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).\n",
    "\n",
    "The goal of this assignment is to progressively train deeper and more accurate models using TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
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    "id": "JLpLa8Jt7Vu4"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from six.moves import cPickle as pickle\n",
    "from six.moves import range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1HrCK6e17WzV"
   },
   "source": [
    "First reload the data we generated in `1_notmnist.ipynb`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 19456,
     "status": "ok",
     "timestamp": 1449847956073,
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    },
    "id": "y3-cj1bpmuxc",
    "outputId": "0ddb1607-1fc4-4ddb-de28-6c7ab7fb0c33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set (200000, 28, 28) (200000,)\n",
      "Validation set (10000, 28, 28) (10000,)\n",
      "Test set (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    train_dataset = save['train_dataset']\n",
    "    train_labels = save['train_labels']\n",
    "    valid_dataset = save['valid_dataset']\n",
    "    valid_labels = save['valid_labels']\n",
    "    test_dataset = save['test_dataset']\n",
    "    test_labels = save['test_labels']\n",
    "    del save  # hint to help gc free up memory\n",
    "    print('Training set', train_dataset.shape, train_labels.shape)\n",
    "    print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "    print('Test set', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.float32, numpy.float32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_dataset[0][0]), type(train_labels[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L7aHrm6nGDMB"
   },
   "source": [
    "Reformat into a shape that's more adapted to the models we're going to train:\n",
    "- data as a flat matrix,\n",
    "- labels as float 1-hot encodings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 19723,
     "status": "ok",
     "timestamp": 1449847956364,
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    },
    "id": "IRSyYiIIGIzS",
    "outputId": "2ba0fc75-1487-4ace-a562-cf81cae82793"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set (200000, 784) (200000, 10)\n",
      "Validation set (10000, 784) (10000, 10)\n",
      "Test set (10000, 784) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "image_size = 28\n",
    "num_labels = 10\n",
    "\n",
    "def reformat(dataset, labels):\n",
    "    dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n",
    "    # Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]\n",
    "    labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
    "    return dataset, labels\n",
    "train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
    "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
    "test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
    "print('Training set', train_dataset.shape, train_labels.shape)\n",
    "print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
    "print('Test set', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nCLVqyQ5vPPH"
   },
   "source": [
    "We're first going to train a multinomial logistic regression (sigmoid regression) using simple gradient descent.\n",
    "\n",
    "TensorFlow works like this:\n",
    "* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\n",
    "\n",
    "      with graph.as_default():\n",
    "          ...\n",
    "\n",
    "* Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:\n",
    "\n",
    "      with tf.Session(graph=graph) as session:\n",
    "          ...\n",
    "\n",
    "Let's load all the data into TensorFlow and build the computation graph corresponding to our training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "id": "Nfv39qvtvOl_"
   },
   "outputs": [],
   "source": [
    "# With gradient descent training, even this much data is prohibitive.\n",
    "# Subset the training data for faster turnaround.\n",
    "train_subset = 10000\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "    # Input data.\n",
    "    # Load the training, validation and test data into constants that are\n",
    "    # attached to the graph.\n",
    "    tf_train_dataset = tf.constant(train_dataset[:train_subset, :])\n",
    "    tf_train_labels = tf.constant(train_labels[:train_subset])\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "    # Variables.\n",
    "    # These are the parameters that we are going to be training. The weight\n",
    "    # matrix will be initialized using random values following a (truncated)\n",
    "    # normal distribution. The biases get initialized to zero.\n",
    "    weights = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "    biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "    # Training computation.\n",
    "    # We multiply the inputs with the weight matrix, and add biases. We compute\n",
    "    # the softmax and cross-entropy (it's one operation in TensorFlow, because\n",
    "    # it's very common, and it can be optimized). We take the average of this\n",
    "    # cross-entropy across all training examples: that's our loss.\n",
    "    logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "  \n",
    "    # Optimizer.\n",
    "    # We are going to find the minimum of this loss using gradient descent.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    # These are not part of training, but merely here so that we can report\n",
    "    # accuracy figures as we train.\n",
    "    train_prediction = tf.nn.softmax(logits)\n",
    "    valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KQcL4uqISHjP"
   },
   "source": [
    "Let's run this computation and iterate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "V_accur_list = [] #verification accuracy list\n",
    "step_list = [] # step number list\n",
    "cost_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 9
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 57454,
     "status": "ok",
     "timestamp": 1449847994134,
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    },
    "id": "z2cjdenH869W",
    "outputId": "4c037ba1-b526-4d8e-e632-91e2a0333267"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Loss at step 0: 17.683537\n",
      "Training accuracy: 7.7%\n",
      "Validation accuracy: 9.3%\n",
      "Loss at step 10: 5.663471\n",
      "Training accuracy: 37.1%\n",
      "Validation accuracy: 39.4%\n",
      "Loss at step 20: 3.798984\n",
      "Training accuracy: 53.9%\n",
      "Validation accuracy: 54.8%\n",
      "Loss at step 30: 3.218525\n",
      "Training accuracy: 61.4%\n",
      "Validation accuracy: 60.9%\n",
      "Loss at step 40: 2.931817\n",
      "Training accuracy: 64.8%\n",
      "Validation accuracy: 64.0%\n",
      "Loss at step 50: 2.748677\n",
      "Training accuracy: 66.7%\n",
      "Validation accuracy: 65.9%\n",
      "Loss at step 60: 2.613341\n",
      "Training accuracy: 68.0%\n",
      "Validation accuracy: 67.1%\n",
      "Loss at step 70: 2.505844\n",
      "Training accuracy: 69.0%\n",
      "Validation accuracy: 68.1%\n",
      "Loss at step 80: 2.416703\n",
      "Training accuracy: 69.9%\n",
      "Validation accuracy: 68.9%\n",
      "Loss at step 90: 2.340463\n",
      "Training accuracy: 70.4%\n",
      "Validation accuracy: 69.6%\n",
      "Loss at step 100: 2.273911\n",
      "Training accuracy: 70.8%\n",
      "Validation accuracy: 70.1%\n",
      "Loss at step 110: 2.214990\n",
      "Training accuracy: 71.2%\n",
      "Validation accuracy: 70.6%\n",
      "Loss at step 120: 2.162206\n",
      "Training accuracy: 71.7%\n",
      "Validation accuracy: 71.1%\n",
      "Loss at step 130: 2.114390\n",
      "Training accuracy: 72.0%\n",
      "Validation accuracy: 71.5%\n",
      "Loss at step 140: 2.070638\n",
      "Training accuracy: 72.2%\n",
      "Validation accuracy: 71.6%\n",
      "Loss at step 150: 2.030254\n",
      "Training accuracy: 72.5%\n",
      "Validation accuracy: 71.9%\n",
      "Loss at step 160: 1.992711\n",
      "Training accuracy: 72.7%\n",
      "Validation accuracy: 72.2%\n",
      "Loss at step 170: 1.957613\n",
      "Training accuracy: 72.9%\n",
      "Validation accuracy: 72.3%\n",
      "Loss at step 180: 1.924648\n",
      "Training accuracy: 73.0%\n",
      "Validation accuracy: 72.4%\n",
      "Loss at step 190: 1.893574\n",
      "Training accuracy: 73.2%\n",
      "Validation accuracy: 72.6%\n",
      "Loss at step 200: 1.864191\n",
      "Training accuracy: 73.4%\n",
      "Validation accuracy: 72.7%\n",
      "Loss at step 210: 1.836329\n",
      "Training accuracy: 73.7%\n",
      "Validation accuracy: 72.8%\n",
      "Loss at step 220: 1.809841\n",
      "Training accuracy: 73.8%\n",
      "Validation accuracy: 72.9%\n",
      "Loss at step 230: 1.784600\n",
      "Training accuracy: 73.9%\n",
      "Validation accuracy: 73.0%\n",
      "Loss at step 240: 1.760497\n",
      "Training accuracy: 74.1%\n",
      "Validation accuracy: 73.0%\n",
      "Loss at step 250: 1.737432\n",
      "Training accuracy: 74.2%\n",
      "Validation accuracy: 73.1%\n",
      "Loss at step 260: 1.715320\n",
      "Training accuracy: 74.2%\n",
      "Validation accuracy: 73.2%\n",
      "Loss at step 270: 1.694088\n",
      "Training accuracy: 74.4%\n",
      "Validation accuracy: 73.3%\n",
      "Loss at step 280: 1.673669\n",
      "Training accuracy: 74.5%\n",
      "Validation accuracy: 73.4%\n",
      "Loss at step 290: 1.654006\n",
      "Training accuracy: 74.6%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 300: 1.635049\n",
      "Training accuracy: 74.7%\n",
      "Validation accuracy: 73.6%\n",
      "Loss at step 310: 1.616750\n",
      "Training accuracy: 74.8%\n",
      "Validation accuracy: 73.7%\n",
      "Loss at step 320: 1.599074\n",
      "Training accuracy: 74.9%\n",
      "Validation accuracy: 73.8%\n",
      "Loss at step 330: 1.581981\n",
      "Training accuracy: 75.0%\n",
      "Validation accuracy: 73.8%\n",
      "Loss at step 340: 1.565438\n",
      "Training accuracy: 75.1%\n",
      "Validation accuracy: 73.9%\n",
      "Loss at step 350: 1.549419\n",
      "Training accuracy: 75.1%\n",
      "Validation accuracy: 73.9%\n",
      "Loss at step 360: 1.533894\n",
      "Training accuracy: 75.2%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 370: 1.518840\n",
      "Training accuracy: 75.2%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 380: 1.504234\n",
      "Training accuracy: 75.2%\n",
      "Validation accuracy: 74.1%\n",
      "Loss at step 390: 1.490054\n",
      "Training accuracy: 75.3%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 400: 1.476280\n",
      "Training accuracy: 75.4%\n",
      "Validation accuracy: 74.1%\n",
      "Loss at step 410: 1.462891\n",
      "Training accuracy: 75.5%\n",
      "Validation accuracy: 74.2%\n",
      "Loss at step 420: 1.449874\n",
      "Training accuracy: 75.5%\n",
      "Validation accuracy: 74.3%\n",
      "Loss at step 430: 1.437207\n",
      "Training accuracy: 75.6%\n",
      "Validation accuracy: 74.3%\n",
      "Loss at step 440: 1.424880\n",
      "Training accuracy: 75.7%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 450: 1.412872\n",
      "Training accuracy: 75.8%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 460: 1.401174\n",
      "Training accuracy: 75.9%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 470: 1.389770\n",
      "Training accuracy: 76.0%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 480: 1.378649\n",
      "Training accuracy: 76.1%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 490: 1.367799\n",
      "Training accuracy: 76.2%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 500: 1.357209\n",
      "Training accuracy: 76.2%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 510: 1.346868\n",
      "Training accuracy: 76.3%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 520: 1.336769\n",
      "Training accuracy: 76.3%\n",
      "Validation accuracy: 74.4%\n",
      "Loss at step 530: 1.326901\n",
      "Training accuracy: 76.4%\n",
      "Validation accuracy: 74.5%\n",
      "Loss at step 540: 1.317254\n",
      "Training accuracy: 76.5%\n",
      "Validation accuracy: 74.5%\n",
      "Loss at step 550: 1.307821\n",
      "Training accuracy: 76.5%\n",
      "Validation accuracy: 74.6%\n",
      "Loss at step 560: 1.298594\n",
      "Training accuracy: 76.5%\n",
      "Validation accuracy: 74.6%\n",
      "Loss at step 570: 1.289566\n",
      "Training accuracy: 76.6%\n",
      "Validation accuracy: 74.6%\n",
      "Loss at step 580: 1.280730\n",
      "Training accuracy: 76.7%\n",
      "Validation accuracy: 74.7%\n",
      "Loss at step 590: 1.272078\n",
      "Training accuracy: 76.8%\n",
      "Validation accuracy: 74.8%\n",
      "Loss at step 600: 1.263604\n",
      "Training accuracy: 76.8%\n",
      "Validation accuracy: 74.8%\n",
      "Loss at step 610: 1.255302\n",
      "Training accuracy: 76.9%\n",
      "Validation accuracy: 74.9%\n",
      "Loss at step 620: 1.247168\n",
      "Training accuracy: 76.9%\n",
      "Validation accuracy: 74.8%\n",
      "Loss at step 630: 1.239192\n",
      "Training accuracy: 77.0%\n",
      "Validation accuracy: 74.9%\n",
      "Loss at step 640: 1.231372\n",
      "Training accuracy: 77.0%\n",
      "Validation accuracy: 74.9%\n",
      "Loss at step 650: 1.223701\n",
      "Training accuracy: 77.0%\n",
      "Validation accuracy: 74.9%\n",
      "Loss at step 660: 1.216176\n",
      "Training accuracy: 77.1%\n",
      "Validation accuracy: 74.9%\n",
      "Loss at step 670: 1.208791\n",
      "Training accuracy: 77.1%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 680: 1.201543\n",
      "Training accuracy: 77.2%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 690: 1.194426\n",
      "Training accuracy: 77.3%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 700: 1.187438\n",
      "Training accuracy: 77.3%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 710: 1.180572\n",
      "Training accuracy: 77.4%\n",
      "Validation accuracy: 75.1%\n",
      "Loss at step 720: 1.173827\n",
      "Training accuracy: 77.4%\n",
      "Validation accuracy: 75.2%\n",
      "Loss at step 730: 1.167199\n",
      "Training accuracy: 77.5%\n",
      "Validation accuracy: 75.2%\n",
      "Loss at step 740: 1.160683\n",
      "Training accuracy: 77.5%\n",
      "Validation accuracy: 75.2%\n",
      "Loss at step 750: 1.154278\n",
      "Training accuracy: 77.6%\n",
      "Validation accuracy: 75.2%\n",
      "Loss at step 760: 1.147979\n",
      "Training accuracy: 77.7%\n",
      "Validation accuracy: 75.2%\n",
      "Loss at step 770: 1.141785\n",
      "Training accuracy: 77.7%\n",
      "Validation accuracy: 75.3%\n",
      "Loss at step 780: 1.135690\n",
      "Training accuracy: 77.8%\n",
      "Validation accuracy: 75.3%\n",
      "Loss at step 790: 1.129695\n",
      "Training accuracy: 77.8%\n",
      "Validation accuracy: 75.3%\n",
      "Loss at step 800: 1.123796\n",
      "Training accuracy: 77.8%\n",
      "Validation accuracy: 75.4%\n",
      "Test accuracy: 82.6%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 801\n",
    "\n",
    "def accuracy(predictions, labels):\n",
    "    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    # This is a one-time operation which ensures the parameters get initialized as\n",
    "    # we described in the graph: random weights for the matrix, zeros for the\n",
    "    # biases. \n",
    "    tf.initialize_all_variables().run()\n",
    "    print('Initialized')\n",
    "    for step in range(num_steps):\n",
    "        # Run the computations. We tell .run() that we want to run the optimizer,\n",
    "        # and get the loss value and the training predictions returned as numpy\n",
    "        # arrays.\n",
    "        _, l, predictions = session.run([optimizer, loss, train_prediction])\n",
    "        if (step % 10 == 0):\n",
    "            cost_list.append(l)\n",
    "            step_list.append(step)\n",
    "            print('Loss at step %d: %f' % (step, l))\n",
    "            print('Training accuracy: %.1f%%' % accuracy(\n",
    "            predictions, train_labels[:train_subset, :]))\n",
    "            # Calling .eval() on valid_prediction is basically like calling run(), but\n",
    "            # just to get that one numpy array. Note that it recomputes all its graph\n",
    "            # dependencies.\n",
    "            valid_accuracy = accuracy(valid_prediction.eval(), valid_labels)\n",
    "            V_accur_list.append(valid_accuracy)\n",
    "            print('Validation accuracy: %.1f%%' % valid_accuracy)\n",
    "    test_accuracy = accuracy(test_prediction.eval(), test_labels)\n",
    "    print('Test accuracy: %.1f%%' % test_accuracy)\n",
    "    #This line below allows for taking the predictions to a pandas dataframe and eventually a heatmap/confusion matrix\n",
    "    pred = test_prediction.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VVq4DGC3pmLTtGDMrejfVm5v74+M9nPI8GgmcmcZ8AtwzLs9/gbXN7F16Tiu8\nnI7BQ84tSmePoUXM7Ot8QTP7m6RZ8PxjHfhv4y7ALpI+wb1Fi9ckU/CtbWbvVOqAmR2XQgdukTb9\nCP8tP03SO/j9m83dEvAqPrbZfdvdPJHV2Bz/PV0h9X86fJyOTr+nXwOz0FmuUQIeoXpuq1bP5964\nPxqhODYzA6cCJ6c5D/5czhs5l/Dwnd1WMAdBEARBEPR3wsMrCIIgCIKgPcgsuauShKKXF8o+Y2Zj\netq4mX0CDMFzq5Ryr4mB2XCBYwkXeB5Ec15Y9c7rE+Dg9DUTBE+Hh+ga3Gx9TZRp+jgzuwu38r+R\nzuM0AO/vNIXtjwJDzKw3Qhk2e45151it/WZ2BS4o/5zy+YF7Gs6Oe6Lkz/1WXGkzKlfNfEmQ31Tb\nPSA75zlxD5JmXnPTWYEHQPLQ+hmejy0Ld5bdK7PjCoP8OLwIrGNmNT2NzOxj4Fe4ork4t+bABerZ\ntrNxrx4oX4dOSpMK41Cr3SG4h1m+3Qnwe3/O1Ids+1hcSbJyCrVaqc5r8Xsk379ZceXFUs30L9U3\n1sxG4J5Pn+T6MmGqd+ZC38cA/2dm99Wqtwl6PDdTPrLtKSvPOvA5dmCV8nsBm+Celflzmwa/JpMX\ntt8ODDazh+r0Y2vgUHy+5MdxNjrP3fuB5YB8OMhuzbEaffkM91Y+DFdQ5n9XZsLn/YR0nnunA0PN\n7IMqdbZ0PvfG/dEIhbHJ/+Zmyq+ZKXt0lfBcYXsDq5nZ53Wq75XnYxAEQRAEQV8QHl5BEARBEAT9\nn1KVz5UYDWyW+35eE/XXrDuF3fq5pHWBjYClcaHaF7jQ9Qbgn1mIphRdq15/G/IqMrO/SHoW2AG3\noJ8utfu+pClyArxG6uuuJ1Oj4/QynsNraVzpMBQXzE4PfAW8gguLLzGzG6tU0622K5Rvhnpt1K3T\nzM6VdAuwLa6cES6A/wb3OnkO97641MzuBUhhvg7LVbMdZQVnw213g57WWfX4JGwfnkLobYx7YQwC\nBuIeH6/hyuPLgSvMbGwjDZrZR8AwSb/AlR1DcSXRADxU3R3AP8zsXkk/Khz+SZ3zqHU+7wPrSPpp\nOp+fA/MC06bj3sPn9Y34tX2mgdNZA9gt1TcX7p1WqY8Nz30zO0bSmXhIx1WBhXDvmrHAO/h9d5GZ\nXdlA/5qh2fuzImZ2v6TT6GwwsLukUWb2VIXyF0q6FNgQP9+lcUXQlLgHbPZbM9rM7m6iHwdJOgcf\nx9VwZf0/KvQSAAAgAElEQVS0uIfYw8B5mUdqIXRfvTnWNOneOFDSCficXwl/BgzElekfA8/goRtH\npjCD9eps6Xzuhfuj0WdNNjYn4h7YK1Ge8xPioUMfBa7Dr9mnddqt22ZP+xwEQRAEQdDbdJRKsR4J\ngiAIgiAIgiD4IZHyOWX5wErAGmZ23XjsUvADRNJbeOjDEnB08jwLgiAIgiAIgvFCeHgFQRAEQRAE\nQRD0UyRNBKwOvAC8aGZfNnjoAoXv1tKOBT8oJK2Ie0u9kML0NXLMdLhHWWZFG3MsCIIgCIIgGK9E\nDq8gCIIgCIIgCIL+y/fApcDjwOeSdm/wuJ1yn98zsxdb3rPgh8S+wAPAh5L+1eAxO9A591+rcqIF\nQRAEQRAEQbcIhVcQBEEQBEEQBEE/xcxKuLKrlF7bSZqy1jGStsPz+WTHNJLLL/jfZkx6LwGrSFqk\nVmFJPwP2pOzdNcbMnq5xSBAEQRAEQRD0OqHwCoIgCIIgCIIg6N/8k7IXzXzA3ZI2kjQgX0jSgpJO\nAE7BFREdwHvAX/qys0FbMhIYmz5PDNwsaXdJA/OFJM2cvAxvBKbG59j3wB592dkgCIIgCIIgqERH\nqVSqXyoIgiAIgiAIgiAYL0iaALgJGEpZ8ZXxIfAZMBCYorDvY2B1M7u/t/sYtD+SDgb2o+sc+xx4\nH5gKmL6wbyywq5md3Ps9DIIgCIIgCILahMIrCIIgCIIgCIKgnyNpEuBoPG/ShLldeeVE/s/d3cA2\nZvZcH3Qv+IEgaXvcI3Da3OZqc+wVYEczu6Ev+hYEQRAEQRAE9QiFVxAEQRAEQRAEQZsgaU5gU2AI\nsBDucTMR7uX1InAfcImZ3TXeOhm0NZKmBoYBqwKLATMBA4AvgNeBR4ArgcvM7Pvx1c8gCIIgCIIg\nKBIKryAIgiAIgiAIgiAIgiAIgiAIgqCtmWB8dyAIgiAIgiAIgiAIgiAIgiAIgiAIekIovIIgCIIg\nCIIgCIIgCIIgCIIgCIK2JhReQRAEQRAEQRAEQRAEQRAEQRAEQVsTCq8gCIIgCIIgCIIgCIIgCIIg\nCIKgrQmFVxAEQRAEQRAEQRAEQRAEQRAEQdDWhMIrCIIgCIIgCIIgCIIgCIIgCIIgaGtC4RUEQRAE\nQRAEQRAEQRAEQRAEQRC0NaHwCoIgCIIgCIIgCIIgCIIgCIIgCNqaicZ3B4Ig6DmSlgB2AJYDfgx8\nBzwBnAecZmbf90EfJgB2BP5pZl/2dnuNImk5YBfgl8AMwCfAGGAUMMrMSoXyUwObmdnf+7qvQRAE\nQdAMkq4HVgbWNrOrapSbAHgLGADMbGZfNNHGisBNwNFmtmfadi6wKbCImT1V5/irgdWB2c3szUbb\nLdSxKXCnmb2Wvm8NnAHsYmYnd6fOVpLWGrenr4ua2ZPjsTtBE0iaBF8nbgQsAEwCvAncChxnZk9X\nOGYwMMDMbuvLvgZBEAS9R3+QqaR+TAgcDowApgXMzBbvhXbmBF4CrjCz9Vpdf4N9uB0fb4BlzOze\nGmUfAxYBXjazubvZ3qTAzmZ2bIPlxwJjzGzJ7rRXo97pgSeBnczs8tz2bfA1iYCvgLuA/czssQp1\nbAT8DlgMmBB4BjjFzM5osA8rADdX2f22mc1aKL8dsDMwP/AecDVwWHFtL0nAmcCSwPPAvpX+o0i6\nF3jDzDassG9F4BJgATN7p5HzCYIi4eEVBG2MpA5JhwAPApsBTwEnARcAs6XPN6UHe28zGjgBmLgP\n2moISXvgAqhlgeuBY4CrcIHGSOCqtKDM8xywdR92MwiCIAi6y8j0PqxOuZWBGYGLm1F21eBS4CDg\n3QbKltKrW0g6BjgXmCq3+eHU/n+6W2+L+Q3wOX6e247nvgQNImlK4D7gKFywORI4EXgc2AJ4NAmU\n8sesC9wDzNennQ2CIAh6hX4mUwHYBtgD+Ag4Dji7l9r5GF9LXdBL9TdCKfeqqnSTNC+u7Or2ejJx\nJ7BfE+UPAk7tYZuVOA54rqDsOgw4HZgutfkvfP1+T1LGkiu7L37d5sINuf+RjjtN0t8a7MNP8PE8\nBT/P/OvoQnt/T32aCTgLV5RtBvxb0jyFei/C5W2n4GvjSyUtUqhvDeBnwP6VOmZmt+BrrfFu1Ba0\nL+HhFQTtzb74A/teYAMzezvbIWli3LJiBP4HfuNe7stMvVx/U0iaCzgCH5sVzezr3L5JgMuA1YCd\ncOFGxozAG33Y1SAIgiDoLpcDnwJrSprMzL6qUm5T/E/t2a1o1MyuAK5oRV0NMBMFAYeZPQI80kft\n10TSZMAGwA3AvMCmkv5oZt+O354FDbAfsDiwo5mdnt8h6SfA3cA/JN1oZh+nXf1qvRsEQRD0mP4k\nUwFYAl/37NybnsRm9glwSG/V3yRvA+sCf6iyf0PgW6CnXnZNPcPNrOXjI2kIriwamts2E7An8CKw\nhJl9lrafh68vjwZWTNtmBw4AXgB+lq1PJO2Nr1t+K+lsMxtTpyuLpfe9zOy/dfq7I/AssKyZvZe2\nHw/8G1fSZX1bClgU2MjMLpE0AHgdNwb7Xa7aQ4DRZvZMjf7tBzwsaXUzu7bOuQRBF8LDKwjaFEnz\n4RYR7wCr5xdmAEnQshXwCrBBci3uCzr6qJ16rI735fS8sgvAzL4Bdkv7x4v7fhAEQRD0lKTgugSY\nEvh1pTLpz+baeAiYu/qwe/8rrIt7n92EG9NMD6w/XnsUNMqvgS+Lyi4AM3sUF+JMAayS29VB/1nr\nBkEQBD2gn8pUJkvvH/RBW/2FK4BBydikEhvg66yvq+xvJ/4EPG5md+a2LYE7pFyRKbsAzOwmfO4N\nzpVdM5U9NmeMQ4rgcAy+RlmtgX4sBrxSS9mV2BhXwO6fKbtSe4/iSuChues2Vyr7WCrzJa4omys7\nLnnOLwwcWKvRpLC7B9ingXMJgi6Eh1cQtC+b4/fwSWb2aaUCZvadpJ2BgcD7+X2ShgG7UnZlfgw4\nwcwuLJSbB/grsDQwM54D5FrgkCyeboptXMIfrh9Jut3MVij2R9JEuPXOl2Y2R4X9pwLbAUub2YON\ntF2DiVN/Fq0yNs9J2gDP05BZrtyWzmPxdE4HZVY9kn6EP5TXxL3A3sTdtQ/LLxIknY2HNvoRcCyw\nBjAWd5/ft5jrRNJvU3mlth/Fr8Mldc4vCIIgCMD/bG6F/yG9tML+tXGFWDE8yZR4yJx1gXnwNcVr\nqY5DauXjlDQK2IRcDq+UJ+yPqS9z4H9wq/6ZlbQlvpZZDFcqvA/cgv+hfiWVeQ0PJ1QCnpD0vJnN\nn3IcnE4hh5ekn+N/jJcBJsetX0cBx+Q9riTdjVv5roCHs1sZFzA9gOdKuLtavyvwm/R+AzApHgpm\nG6qECEprm/2AlfDcHC/ioWj+ns8P0kg5SW8DYyvkWVgFuA443Mz2SdvuB6YB9sI92wfiIS63SPu3\nTueyKOXrcRN+PV4r1L8EbhGfjbPha5dz0/57cOHMj83sjcKx2+FhcTYzs/MqjM9wPF/KgWZ2aGHf\n5HjeiKfNbKm07Td4zpUF8Tn8JHCGmZ1ZrLsCEwMDJM1rZs9X2H8ScAce5gpJo/HwoSXgVEmnALOY\n2btp/yr4+P4Uz6cxBjjSzK7MncOkwJf4tbwYz9OyIL7GPQf4S2GuzoKvhZfF74X38FBCB5vZyw2c\nYxAEQVCdvpKpjMW97M/Af9N/inss3Yh72LySy6lFqmuMpBKwPK4wOAv4vZmdUKj7djwP1rTZOUj6\nKXAwrkiZHngVN8r5S857qGIOL0kz42uZ1XGZxjvANfhzJ+/9dhDuabRgGsdNU/nngRPN7LRK41mF\nS3EvovVweUj+/OZK53Einpedwv4pgN3TsfPgz/bX8CgIB5vZF7lzLQEd2fUws63S+M2JR/45BZf1\nXGVmG+dzeKWwio/iIZAXMLO3cn24AV+vbWpmo6udpKSF8DXnnoVdmXJzzkL5yfBQhe/lNj+EK2lv\nrdBEphCcslofUr0TAAvha9d6ZMqqf1fYl+UWWwYfm48qtD81Pieydg/Cx/4l6nMe8HdJPzezSu0H\nQVXCwysI2pdV0/uNtQqZ2bVmdo6ZjbMQknQ0nnNrEP4QOT99Hi3pr7lyA/EH6Wq4MugYPHHrjsBt\nufxXB+GLqBK+gDu7Sl++Ay4EZpXUabGS6lrPi9mDTbRdjSwB5+6SRkpaIYUkyPfnMjO7P319OZ1H\nB66UO5CUgF7SHLiwY7v0fiyeFHRP4PZkPZ+RxaC+DndTPxMXGK0B3C1pnAJO0l7A8enrqfgidh7g\nIkmb1jm/IAiCICB5bb0ErJ7+9BfZFDe8OCfbkAxQbsP/ML+OC/b/iSsv9sKfXbWolJfrPHwN8BUu\nMHgTF2D8rHhwyi9wJv6H+J+p/bfxkEG35Z7Xx+D5lMDj+GfPzC7tJyOWu4Ff4Xk7T03n/Rfg+sK6\noYT/Ab8HtzI9C8+VsCxwo6T565x/1uaPUnsPmNnLZmb4H/6hSUBTLL8Evo4YgecfOxn4Bvgb8Pdm\nyxXHoA4l3HjoPHx9NRI/fySdhAvhBlC+Hu/gCrBb03zJ+rYqHvZpdVxBeRru4TYyrWugnFtueIV+\njAD+iwveKnFF2r9RhX1rpT5mirXN8TXn1Ph8Og0XSJ4hqVpYpDw34eu+OyTtmYRZ4zCzl8zsylxC\n9ovxJO2k94NSX0nC0GtxA6bzU19mBa6Q9PsKbS+NCxA/wK/pp/jac1yo0KTguxFXst2P3w/34aGQ\n7klK6yAIgqD79LpMJcdS+NrrW/x3/1H8WXdzWvdkObUyhc+p6fvL6Xu1Z36nNVHyWrsZNzy5Es8X\n9Ra+vru8UgW5Y+fGjTW2BZ7Gc7Q/DWwPPCRpUIV2R+E50K/BjZFmBU5OhjSN8iDuyVQp+k4WzrBL\nKO20trsFf36+iY/rmbgR0x8py6Wysf0UX6fmn7clYAbcUOlOfE2Y974CIBnG7IuvecYpHSVtjyu7\nLqil7EoMT+11mm9m9iCuyFpX0q6SppH0Y3xsp8Kf/1nZ/5jZX9Kas8h6qf4n6/RDuJHWV5LOkfS6\npM8l3ZWMd/JkSrRKOeymwddRmaLu0VR+d0lTyfOeLoivz8HXlXMBhxYrqsINqf5K68kgqEl4eAVB\n+zJ7en+2mYMkLYNbwDwErGJmH6btM+ALsD0lXZOsmzdO7WxpZnlB2Ym4BczKwHVmdoik5YEfA0dU\ns45KjMKVVsNIgpbESriQIls8NNR2tUbM7Al5HOO/4MKVzYAvk4XzTcClZvZcrvwrwCHJUuntglXx\nqcAswBpmdn2uL7uk/h4I7J0r34FbUi1mZh+lsuvigr/jcYty8BjVz+MebaVU7ijgOdxSrIvlcxAE\nQRBU4FxcebUWLnwBQNL0+PPyzoI3yDBgSdyT+dBc+b1xr6j1JU1sDeahkrRSqvMqYP1k4JJ5MR9P\nZ0HMHMAuwC1mtlKhnuvx9cAvgdvN7G/JSnkR4OSCl3RH7rhpcI+ZT4GhZvZ42j5hGpth+DP3iNzx\nM+LhIIeb2dhU/mlcIDICt1quxwjckycv4Dg/tbM1XROjn4Yr+dbM1hOSOnDB1LaS/pbyGTRarlmm\nBv5sZuPOLSnmdgRuMLNOIXAk3Yxblg/GjXYmwsf5W2CIeS41JB2AC6sOknvrX4hf9+HkPAvTtf8l\ncE41D0Iz+1LS5cAISQsVrvnGeP6OzHvuj8CHwE8tha+WJ31/Fp9jnbwaK7Af8H+45fjhwOGSXscN\nnq4D/pVCBGV9u0zSjLgR09WWQiGmMTyWpOzMWdjvh3uIHSnp6oIX2aLA0Wa2Vyo7Ia50XU3SRmZ2\nEW70tTCwt5kdmRvHffH8FxvigrkgCIKge/SFTCVjYeCPZnZsrp5s3bO8md2IyyPmwr3fTzWzx1I5\naDyc7vb48355y4XNk3QVbhy1oJk9XeXYM/D10TZmdlbu2O1xY6YzUn8zMrnHgrkxGI3LebamvgFV\nnsuA36ur1/X6wM1m9rG6RpTcADesOszMxkUVSAY4zwPryHPcfoKP7ZbANEUPctyz/RgzK3peFTke\nf/aulwyAnsbXGm/gMqp6DMEVQo9X2LcKvsb6W3qBG27tamZ/r1C+E0keNwx4lzqKTcr5uzbEr9Uo\n/F5YB7hW0tZmdnYq8yAe5Wg9Oq+jwf93lHDFF2b2XloTHo5HgigBd+H5UCfC19anm9nr9c4n1feS\npA/J5TsLgkYJD68gaF+mTe+f1SzVlS3xB88fskUJQLJW2htftGyVNmd5CpZK7scZ++AhXKoqnKph\nZvfhYXk2SMKbjCxEzPmtajsJB5bBFU2f45Y+Q3El2DOSzpC7iVdF7tK/KnBtXtmV+DvuLr9FYXsJ\nODRTdqW+XI5btgyRh6cB/w2eEU9yn5V7A1gAtzIPgiAIgkY4B39mFpOpD8MN3M4ubH8Qt949Mb8x\nhbl5JB0zXRPtZxar+2XKrlTfibgRR54vcCOU3SrUc0d6byqpOP4nfGo8n8E4IYJ56L/f48KFSpbG\nx2bKrsS1+DgOarDdzXAFTD500Wh8LDbPr3OS1fRSuKJk3HoiGbzsjSvaxjZarsH+VaLoWfVZOo89\nKpQtXo/lcMvtMzNlV+rbl7jg72Bg8iRYuhIPEZ33lhuR3s+t08dR+HUYlm2QNBUuDLrVymGtO3DL\n50VyffkYV2AtUKcNUj8H43PkEfy6zZb6eR7woqRGcr1mIbH2yxt9pXE5OO3brHDMx7jSKiv7PR45\noAP3yoTyf/XFC1EKjgVmzwsjgyAIgm7RFzKVjC/JeQYlMpnGoCbbr0UmR1m6sH1zYMZqyi5Js+NG\nLncWny/m4QkfAFZInkcZJXxNkB+D+/Bn3KAm+30phRzryVDmZ7iHdSUexsNIH5/faGafp30T4gq5\nRqjmeZ6vt4Rf+69Tm//AoyNsZbl8WjVYEng2M3Yu8Dvce/5JXOF1Dr5mPiQZllVF0mKUw5rvUM2o\nKMcAXCG4t5ktZ2Z7m9kIfP35GXCSpGztlxmUHSBpJ0nTS/qxpNPxsIiQU8aa2dG4MdHuuEJy+bTG\n2Q5fT/5Z0gBJ50v6StLHkmp5fD0FLJKPNhAEjRATJgjalw/w0DTT0VxC05/ggpJ7Kuy7O1cG3PL5\nANxKdmN5bOLrcOXPu93pdOI83Kp2CB4ScGI8x8j9ZvZiK9s2D1m4UWrjl7h31RrpHLfGLahruUgv\niT/AZ5BUzEXSgYcYml3SLJaL40wFN3g8LNEvU9tv4RbcewFPS3ognd81ZvZQo+cXBEEQBGb2ojwv\n1cqSps4J3TfB/yxfWihvgEmaVJ73an7c+OKnlA0u6oUOzrMY8G1e2ZTjPjobdnyAh/vpkLQwHupk\nnlTHr7rRNpRzZ9xV3GFm70p6HlhY0oCCEKBo0f1Jeq8UtqUTkhZJfb7dcjktzOx1SXfh4/hryiHw\nsrXV/RQwswdwQVLmEV63XA/olDPBzN4Hzk/XYxE6X49MwJJdj2ycK/XtBjrngjgHtxzelHIut02A\nN82sUt6JPLfg66RhuWPXw6/LqFy50/BQTQ9IGkNaJwL3VhEmdSEpaE8ETkxGTivg553lbL1A0ipm\ndluNapZM76tKKobwzBTHixe2P5wEcvm+PCXpc8pz5To8ZPgw3PPrRsprxbcIgiAIekpfyFQyXskb\nBSU+wWUKddcdTTAS99w+UtKu+HPjOuDGvNdyBbLnVJe1VOIeXCHyE/zZlFE0bAJXkEzVTKfN7F5J\nb+HP+8yruWo4w3TMc8BzaT27NJ3Xs0NTsUbXlI3klMLMnk1eTEemtk42s5vqHScPUzw5hTxwad8I\nPFLDZcDGVo6UcAieO+tSSXNZLqRm7til8es7Da7A+lcD53A2FdKQmNkz8rDj++PyuTPM7K20Nr0I\nXy+dlIq/gHu1ZYq5fD2d1qvJyHxfPFfeu5KOwI3KN8fvvRMkvZDzKsvzPn6PDMTDnwdBQ4SHVxC0\nL5liaN5ahSRNnf7AZ0wNfFVhsUUSkH2BP4hJf6aXwl3RO3BBxSjgbUmnSpqkm30vWu6ujltXjQvh\n10DbnfJx1cPMvjWz283sADNbEn+Af4krw+ascWhm9TUYV8DlX/sDc+PCn6Ll0Bt0JXtAZy7f++BK\ntwdxy6UDcaHN08klPQiCIAga5RxgEpJlbLLA/QVwSVGwnpQbB+BKhfvwP73b4XkNXknFGg2dA/5n\n9fMq+z4sbpDn23oWD+lyIe69PTPl5NfNtA2+toGywqpIloNp8sL2rwvfMyVJI+1vkd6HSBqbf+Ge\nUOBWxxmZ4qNW2OdmynWXLla/kobhlr6P4eEC98atcLM8Itl4NNO36/GwOhunNhbHwznVDdecvO4u\nAOaTlAkMN059vzxX7gQ8/8mdeIjAvXFB3UuS1mmgj8V23zaz881sSzxM9zm4gWi9EEfT4mO0M13X\nir/F51XRY7LSOhE8d1q2TvwvbqF/En7uG+A51t5MltFNCRODIAiCLvS6TCVHcc0Bza07GiKFQfw5\nvr6aFl+LXAa8Iw/7W41WraXAz6s753Q5HuFn1vR9feC2at5TaT27b+rb/biyb3vcKPnlVKzRftTz\niir2M7t29zV4zDTpvZLScYtU3+6FSAkv4oq1KXDlXyck/RoPoTktruw6qsG+1OJhfMzG5aJNRj/z\n4t5tf8Kvy8KU1/jvUJtd8HPIQiJuDfzDzC40s1PxkOjVQkJm/y+aiTwRBKHwCoI25nr8QbRynXLb\n43+MD07fPwMmlzR1saCkSXH35nGWI2b2ipltiws+BlNOCLotHqalaZIlzgN4jpBM8fUdbjWSL1er\n7UOogaSHJD1Sbb+ZXU05pM58Nar6b3o/1MwmrPKayMyKiUEHVKgrU56Ns+oxs7PNbDAu6NsUd9ef\nH7hSnnslCIIgCBrhIlzokBmTbJLez65QNguN9wDuzTKzmc1iZhvgoXqb5SNgikII4owp818k/QIX\nwkyAKyvmMbNpzGxF/E97d8hCEc1WZf90uCDhoyr7myKd53Dc6vg0PNdn8fUVnivjR+mwbD3RRUmR\nBDaTNlkO/JwqjXlRGFXrXJbFw0mPxYUp85jZtGb2K7p6q9fq20T5cDMpfM35wLxJ2bVR6m+9cIYZ\nmXHURmk9tCJwZVICjcPMLjWzobg31ga4kmoW4CJJ89Q479UkvSLp95X2Jyv4HfB7qtY6EXxcSsCs\nNdaKyxWOqbROBF8r5teJ75rZ78xsVtyTbG9cWbwxXUNjBUEQBM3RJzKVFpApVxp65pvZ42Y2HDfK\nXR5XNHwO/Emej6sSjayloLXnVeRS/BzXTWkgBlOQERX4A3AoHpZ4FTz1xSxmtj5lA67e4Az8mnwM\nHCfP3VaPTDk0TYV9swNfm9mrFfY9ic/RfChJJP0GV7xNAmzfjLJL0oKSVqyyO1uffJXfaGafmNk5\nZnaEmV1uZt/gRtslPOxgtbamxA2H/mZmH6U13fS4oVXGs3h0gUpkMrRmFJJBEAqvIGhjzsctV3ap\nZuEpaQCuHCoBN6bNY9L7MhUOWRZ/mD6Rjl9T0t8lTWlmJTN7wDzB53KpXD7PVEOhY3KMAmbABRhr\n4C724/7gN9l2Jb4DFpPHM67HmzX2ZdbmS1XaKelgSXtViClcDGkDbmn/HfCQPPbxgWmhgpm9b2YX\nmNkwPAH55JRD5ARBEARBTZJF8RXA8pKmwZUXr5jZHRWKD8fXEOuY2S1m9l5uX5b7qBnL3IeAian8\n7Ctuy/KMbW9ml5jZy7l9XXIB0Nj6Ykw6psvaJo3FYngkx57kvsqzMq5Uud7Mdqr0wq2pJ8StYaGc\noLyYUwM8jN4XknZrohz4NZyiQrmaluoFsrDO2ybl0cu5fcXr8TiV84KAh6X5UtL6uW1Zbrm18PCO\nj1YwEKqIeY6wp9Ox6+CeVuO8wyRNJmlfSbuk8h8nAcwWwFH42P9fjSbeBuaggsV0BfLrxBJd52S2\nVuwy/5NQ6Uh5cvs8XdaV8nxn05NCRkpaXtLx8rwqmNmj5vlpf44r4iLfaxAEQc/oTZlKQ8+7Bvkm\nvVd65s+d/yJpM0kngIftNbM7zexPuFFILTlKdk6/rLJ/CHWUGy3gDtzoY108YsH3VAlnmBiOy1fW\nNrObrHPqi0rr2WZlVl2QtBMeLvE03It7Rjy3e03M7GtcQTawwu53gEmz532BLBfquHB+KcTgP/Hx\n2cjM/tHEKYAbZt2UDJKKLIuP07hQ25LeVTnkdp718PXI7TXa2h3XPRyTvk9UeAeYjOrXZiBulFXN\nMz4IKhIKryBoU8zsJTxvwYzADQUXe5K10fm40ONKM8viS5+NP/T/KmlgrvyMuIAgb327AB7/eYdC\n85l788u5bd+m90bDHF6AP6CPwhduowr7m2m7Eifh53m+pC6CH3nOkk2AB80sv2j7ltw5JMHPnXju\nhPULdWyGhzVcpRDOoAM4OL9oTuGbhgJXJJf8z/DEpIdJKrpnD0rvvWmVFARBEPzwOAdXPO0ALJG+\nV+Ir/I/mjPmNyXI5+7PdTOjgkfiz7whJ44Qx8pwExRwWmcVocd2yMu4FVGy72voi/8f4Mvy5uksu\nBB7JGOWkdOzIRk+mAX6T2q8Vnu8scknrzZPEPwKsKWloro8T4JavJeCmRsulzc/gFuZDcuUG4pbo\njQp1ql2P1fGQNVC+HjfjApet5fnXsrKTA7/HhU63Z9vNbAxuRLUlsAjV52M1RuEhc36LW5Rfl6v7\nq1TvIfKk9nmytWLVdVRSqN0HDJZ0jAqhstN4H4HPnbNyu77Fr2t+Pp6Lj3dxbT0xLgTbg3KoqIxB\n8twqWdlJcGFQKdfe7Onci15os6b2X652fkEQBEF9+kim0gqeSe+r5b3pk/Kl6F00GF8PbVDYXlOO\nYmav4Z72S0nqJIORtA1uvHurmdUyFu4RyTDpX7iR8xZ4OMMuobFzfIUbuMyU3ygP2z0ofS2uKZtK\njVGod07gcNwQ5k9mdj6ed3TDKgqhIk8Ac6trapCL8Pl0lKRxOceSAmxPXKl0Wdo2G+V1wjAzu5zm\nuXxTaJMAACAASURBVDi9H1Zo75d4CMznKedlfRifY8U5sSseTvo0M6sYBjPJuXYDjjKzz8A91/GI\nC4NzRQfTNa8u8mhQC/ph9m1xfxDUouiREARBe7EvvjjbEs9XcA3+cJoNtz4eiOcy2Dw7wMzuknQs\n/uB5TNJVadcauLDj8NxC7gw8p8cR8pxSj+GLiY1wwdLhub5kFhdnSbrRzE6s1XEze0/STcBqeCiY\nYnLNZtquVP+5kpbAlUpPSroFX2CUcOHbr3ChzfDCoW8AC0g6Gbg2hT7cDld6XSzpulSP8DF7H1fM\nFRHwiKSrcQvitfEwUXuk/n0raX88HM0Tki7H4zkPwa1+z0mhH4MgCIKgUW7ErUT3x5931ZQ8o/CE\n3vdLughXVCyPJyx/B3/ezkCDAnXzROPH4YL5RyRdC8yJe+c8T+cwJRekcqencCpv48/llYH3cm1n\nvIELAY5P64s/p+3jLHbN7JMkjBkF3Jeeqe/iXuQL4wKcY2gBKTTLOvha5Mpq5czsVkmvAPNIGmpm\nt+NChNuAG1MfX8XPexF8/fVEOrzRcmek7f+SdB7lsITPULD4rsFoXKlypqSV8HFbHA912el6pLXL\n1ngInfslXYYrotbCBWk7WNeE6iNx4d93qa1mOA84DPfQOzWFScyzNx4e8xFJl+CW0z/H11LXm9ld\nderfCLgVn48bS7oez2s3PX7+cwPnmlle4ZWtd3+XhE7HmNmTkvYD/oyvOa/Cc6D8Gg+HeAll4VLG\nZ8AxklYBLLW3EHC6md2aylwE7ArsJmlJ4D94aJ8NcaHdQXXOLwiCIKhPb8lU7m5VB81sjKSHcM/l\nuyXdgT8bl8e9gn+eK34k/pwYLc/R+Ryu/FkfV9ScVKOp7XG5x98lrYfLYBbFn1Gvp/15WpZ7LMel\neI6nJSu0V2QUriy5N61nv8HHZAk6r2ez8Hlv4KGWz8UjDDWrlDwTN9beOkVWAJcFPQacLOl2M6sV\nPvsa3INuaSA/P07D11IbAYsmmdP0uKfb1MCOZvZ6KrtX2vYCsESSeRW538xuAEhGUUOB23NRH07F\nPf5WA8ZIugEPmZjlud8ki4pgZq9I+hvwe0n34vNjMWBVPBf9ATXOdy9cWVcMwXxWqm9sOs+lKYdi\nz7NoOtcbK+wLgpqEh1cQtDFmNtbMtsHjFV+DP3h+C6yJ/3neHhiaexhnx/0BGAG8hD9YMuHIema2\nb67cx7h1zSn4H/bf4X/erwYG5wQu4H/y/40rknZu8BRG4QK5K8ysU0zeJtuuiJntjof/uRBXQO2E\nL0hmBf4CLGyeCDTPzvi4bIkvOjCzZ3HB4On4Q3dXfKxHAkubmRXqKOHj+lCq5xf4Q31wbqGCmf0d\nD+30Ir642Rm32N0NX+QFQRAEQcOkP6ej8Pj7dyXL5UrlTsCfqx/iypVhuLJgI8pJo1fPHVIpjFun\n72a2B77u+BIP/bMAbp17Q6Hcw6nuh/E/8tvggqY/4cKNEv4HPONE3LPoZ8CuKuewKrZ/Mb5uuBn/\nE74t7km+O7ByBWVJNQ+oSueaZwNgUuCy5GVUi0zhuE3q4yP4n/pLcYHMb3HL5F0K669Gy12Kj/HL\nuCfZmrgQY0SV8+hyXmb2AC6gG4MLw7bBhUN74QY4na6HmV2Hh7u5PbW3I26pO9zMzqgwBlnujZvN\nrF5S82LfXsUFQiXcwr64/xJ8Lo3BhTS74sKt/XClZL3638DXc3vglsWr4/lA1sOFYxulEIl5bsLX\ngzPi94pSXYenPjyBz5FtcUOmXXHBUXHsn0rlZqfskbeTmY0zokrhj1YGjsZDaO6CX6M7gWVaKUwN\ngiD4X6W3ZSqJWmuLRj2yf42vK+bFnwcDcFnHv/N1mNkruFJlNC7D2A1/bo/E5RFv5+osFY59Hn/2\nn4F71uyc2vsbsGSFdWWtvjd6XsVyt+Br0u9wA5uq5c3sZHws3sflJ8OBT3EZy3apWH49uxceanID\nPH96I30tAUjaDl+TXZfWnFkfnsdlYTNRW5kIbuTdJWdciha0Om7IU6L8vH8AX8Pm11dZyMG5cWVT\npdcqufJD07Zx0QBSeysBB+Meb7uk/ZcAS5nZQ4V+/wGfR1OmsvOkc14x89wqImkmfP78tSjrw9f8\np+H/A5YG9jWzCytUs0o612YNpoKAjlKpxyFMmyaFEjvczJZPMUNPwa3Unk0PmiAIgrZE0ll4qKMl\nzOyxeuWDIAhS2LWRuPXld5QF9WfjHhtPmFmjhgRBEAT9Bklr4fk3NjGzC8Z3f8Y3SWH7JW59/Yvx\n3Z8gCIIgCPqO5L21oJkNGt996e9IehJ4z8yGju++BO1Hn3t4Sfojbi2QWWceABxkZsvx/+zdd3xV\n9f3H8dc5d2cnJCEQAsjwssGFIrgVB1rUWlerrVZ/blv33tZB66qrKm5bR63WKtVqFXGCIAiyLnuv\nQHZy9z2/P25yk1CGSnJvLnk/H488cu/9nu/N55N7D9zcz/1+vuD2er3jkh2TiIiISAodB9h8Pt9o\n4C7iK1AfBG70+XyHAKbX6x2fygBFRH6sxuLOdcTbHv6UPSZEREREdid3A2Ver/eYVAfSkTXuJzaA\n+EoykR8tFS0NlxBftthkFlDYuBldNs0bU4uIiIh0BosAe+NroVzir4X2brH/zPvE28WKiHR4Xq93\noNfr/Y74/hIHEG9nE0xxWCIiIiIp5fP5viTe7vmOVMfSwd0JTPL5fB+lOhBJT0kvePl8vreJt+tp\nspj4BnbziPc8/TTZMYmIiIikUB2wB/G+/08Rf13UchPoWuKFMBGRdLAByCe+sfuDwEOpDafD2dk+\ncSIiIrL7uhTo6fV6T0l1IB2R1+sdC+xF8z5sIj+aPdUBAI8Ao30+30Kv13sx8T+KLk1xTCIiP4nP\n5zsHOCfVcYhIWrkC+MDn893k9XpLiX/4x9liPJv45s0iIh2ez+erBHqlOo6OqHGlmy3VcYiIiEhq\n+Hy+LUC3VMfRUfl8vg+BglTHIemtIxS8thD/5DLAOmCnm/dalmUZhrGzw0RERCS9dZb/7Ctobulc\nRfz12Syv13uIz+ebAhwLfLKzO9HrIxERkd2e/qP/iWavqk37lZXDe2YDcMSjX6c4kl3z8WWjACg5\n/80UR7LrNjxzCtdO8qU6jF02YZwXz0kTUx3GLvO/fR4A89fVpziSXTOoeyYAt3+4OMWR7Lrbx/Zn\nwPX/SXUYu2zhfUfz+JcrUh3GLrtkdG/Oe31uqsPYZRNPGwI7eE3UEQpe5wOve73eMBBqvL5DhmFQ\nXl67s8N2W0VF2Z02/86cO3Tu/JV758wdOnf+nTl3iOffSTwMPOf1ej8DHMD1wLfARK/X6wAWADt9\nR6Azvz7SudJ581funTN36Nz5d+bcoXPn34leG4mIiIj8JCkpePl8vpU0ruRq3LBvTCriEBEREUk1\nn89XD5y2jaFDkxyKiIiIiIiIiEjaMlMdgIiIiIiIiIiIiIiIiMiuUMFLRERERERERERERERE0poK\nXiIiIiIiIiIiIiIiIpLWVPASERERERERERERERGRtJaWBa/3vlhGKBxNdRgiIiIiIiIiIiIiIiLS\nAaRlweupt79n3oqKVIchIiIiIiIiIiIiIiIiHUBaFrwAglrhJSIiIiIiIiIiIiIiIqRxwSsWs1Id\ngoiIiIiIiIiIiIiIiHQAaVvwikZV8BIREREREREREREREZF0LnhphZeIiIiIiIiIiIiIiIiggpeI\niIiIiIiIiIiIiIikufQteEVjqQ5BREREREREREREREREOoD0LXhphZeIiIiIiIiIiIiIiIiggpeI\niIiIiIiIiIiIiIikORW8REREREREREREREREJK2lccFLe3iJiIiIiIiIiIiIiIhIOhe8olrhJSIi\nIiIiIiIiIiIiIulc8FJLQxEREREREREREREREQHsqQ7gp9IKLxERERERERERaQ+WZTHxz/exctli\nHE4nF155M1279Wh1TDAQ4O7rL+Giq2+le49eidsXL5jL3559lNv+9BQAa1Yu4+mH7wGgpLSMC6+8\nBdNMzmfQLcuiaspLhDevxrA5yD/8HOy5xYlx//JZ1Ez/F4ZpI3PgQWQOPgQrGqHi44lEq8sxXB7y\nDzkbe24xUX8NlZ88TyzYAJZFwVHnY88pSkoeAPf/ci8G9cgjGIly5YvfsmpzfWLsqGHduPL4gYSj\nMV77cgV/+2IFNtPgz+fuR1mXDKJRi6te/pZlG+sYUpbHhF/tTTAcZe7qKm55fXbScrAsizn/eJLq\ndSuw2R2MOO0yMruUtDomEgry9VO3stdpl5NVXLrdOdVrl/H9209jmDZMu4O9z7wCV1Zu0nJ55ILR\nDOtdQCAc5aLHP2fFxtrE2HH79uSGU0cQjli89MkiXvivD5tpMPHyQ+hVnE0kFuPiJz5nyboaCnPc\nPHHxGHIzXdhMg98+8ikrN9UlLQ/Lsnjq4XtZsXQRDqeTS66+lZLuW5/rfm6/5hIuvfY2Ssuaz/VF\n87/n5Wce5a6Hnm51/Gf/fZ9///N17nvshWSkAMTzmPHGE1StXY5pd7L/mZeTVbj1cyvA5MdvZf9f\n/o6cxufWtuZUr1/F9NceAyCruDv7n3E5RpL+zQK47cSBDOiWTTAS4+Z/zGNNhT8xdtjAIi46vC+R\naIy3vl3Lm9PX4rAZ3HPKEMoKMqgNRLjznfmsrvAzoFs2N/1sANGYRSgS47o3vqeyPpyUHCzL4tOX\nH6V89TLsDidH/OYKcou7tTomHAzwzwdu5MhzryS/pMd255SvWsq/HrmV/K6lAAw97Hj673dwUvJo\nymXJe09Tv2Elpt1B//EX4Slo/dyKhoLMfelO+p94CRmF3bc7x1+xgUVvPQaGQWbXnvQ7/vw2jzd9\nV3hZKniJiIiIiIiIiEjbm/7lp4TDIe5+5DnOPPdSXvzLQ63Gly1awO1X/R+b1q9tdfu/3niJpx66\nm3C4+U3VV59/gjN/eyl3PjQRLPh26mdJyQEgsGwmVjRC8Sk3kzPqFKq+eDUxZsWiVH/xGkUnXkvR\nSddTN+9Tov4a6ud9iulwU/yLW8g76FdUTnkJgOov3yDDeyDFJ99Azv4nEa5cn7Q8jt2rO067yQn3\nT+YPb83ljlOHJcZspsEdpw7jFw9+xsl/msJZB/ehS5aTI4aWYDMMfnb/pzw4aQE3njQEgD+dvTc3\nvfYdJ/1pCrX+MCeNLEtaHuu/n0osEubgyycwcNzZzH3n2VbjVauX8OXjN1C/ZeNO53z/z4kMO/lC\nRl/8B7oNPYDFH7+ZtDx+tn8vXA6Tw254l1tfns6Ec/ZPjNlMg/vP2Z/jbnufsbe8x2/HeinMcXPM\nPmXYTIPDb3yXe9+YxZ2/3A+AP5w9klenLOHoWyZxx9++xdsjL2l5AEz7YjLhUIj7HnuBs867jOef\neLDV+FLffG7+/flsXL+m1e1vv/YiTzxwF+FwqNXtyxYv5OP332n3uLe2Zs7XRCNhjrryTwz/2a+Z\n+dbEVuMVq5bw8SM3UL95w07nzHnvJYaP/w1HXjEBLFg795uk5XHk4GKcdpMznvyGBz9YzPXjvIkx\nm2lw3Tgv50ycztlPT+fUkWXkZzr4xX49qA9GOf3Jafzh3QXcOn4QADeeMIC7/rmA3zwzg//O28T/\nHdonaXksm/kV0UiYU296mAN/fi6fv/5Uq/FNKxbzj/uvoaZ8/U7nbFqxmL2P/jknXzuBk6+dkNRi\nF8CWBd9gRSKMOP8eeh/5S5Z/8EKr8dp1S5nz3C0EKjfudM6yD16g95FnMvy3d2FZMbYsaPvnVvoW\nvKKxVIcgIiIiIiIiIiJJ4vV6k/Y+1sK53zFivwMB6D9wCMsWLWg1HomEueaOP9G9Z+9Wt5d0L+Pq\n2//U6rarb/sjA4aMIBIOU1W5hYzMrHaNvaXg+kW4ew4FwFXSl/CmFYmxSMU67HldMZ0eDJsdV/c9\nCa71Ea5ch7tXvKDkyC8h0ljYCq1fTLSugvJ3/oh/0VTcpQOSlsf+/QqZPC/+Zuqs5RUM752fGOvf\nLZvlm+qoC0SIRC2mLdnMAXsWsWxjHXabAUCOx0EoEn8vsVueh1nLKwCYvnQL+/crTFoeFcvnUzxg\nHwAKenmpWr2k1XgsGmHkOTeRXVy6/TlrlgKw79nXkNO9NwBWLIbN4UpCBnEHDizho5nxAtD0xeXs\n3bd5pd+AHnksWV9DrT9MJGrx1YKNjBlUwuJ11dht8VM4N8NJKBIFYNTArpR2yeS9247ltIP78tnc\n5BVSARZ8/x17j4yf63sOGspS3/xW4+FImOvvepDSrc71bqVlXHfnA61uq62p5m/PPsFvL72mXWPe\nlvKl8+k+MP48KeztpWL14lbjsWiYg86/ieyuPXY6Z8x5N1HUZxDRSJhATSUOT0aSsoB9eufzuW8z\nAHNWVzOkR/Oqxb5Fmazc3EB9MEokZvHtikpG7lFA365ZfLaoHIAVmxvoU5wJwBV/m82ijfHVgjbT\nIBCOJi2PdYvn0nPIvgCU9B3AxhWtH49oJMzxl91Gfrey7c7Z1Dhn08rFrJg9jTfvu5r/Pv8Q4WAg\nSVnE1axaQH7/EQDklO1J7bqlrcatSIRBZ16Hp7B0B3OWAVC3bim5veMFyYL+e1O5bE6bx5u+BS/t\n4SUiIiIiIiIislvzer19vF7vP71e7xpgmdfrXeX1eid5vd492/PnNjTUk5HRXJiy2WzEYs0fvt5z\n0DAKCothqw5EI8cchs1ma3WbYRhs3rSBq84/jdqaKnr1adfQW7FCfkynp/kG08Sy4nnEwgGMFmOG\nw40V8uMs7EVgxXcABDcsIVpfhWXFiNRuxnRnUTT+GmzZBdR8OylpeWS57dT4m1fNRaMWRryWRbbb\n0WqsPhAhx+OgPhihrDCTL+46mgm/2puJn8SLSyvK69m/f7zINXZ4NzJcrR+v9hQJ+HG4m4sHhmnD\navG8Kug9AE9eF7B2MMcwsWIx3Nnxol/F8gUs/2ISfQ/5Wfsn0Cg7w0F1Q/PKpkgslng8cjKc1LQY\nq/WHycl0Uh8I06trNrMfO4VHLxrDE5PmAdCrKIuKuiDH3/E+azbXcfXJw5OWBzSe6y2K0OZW5/qA\nwcPpUlTc6jEBOOCgw1ud67FYjMf/eCfnXHwlbo8HK8ndySKBhlaFKXOr51bhHgPJyCukZSLbm2MY\nBvUVm3j/nksI1teQX5q8lVGZLjt1gUjiejTWfK5nuluPNQQjZLrtLFhXw6ED4q1ah5flUpwTL/5u\nqYs/D/fqmceZo3ry4hcrk5QFhAINuDIyE9e3fjy69RtEVn4hVovHY+s5Tf8+lPQZwOhTz+eU6/9E\nblEJ0955OTlJNIoG/dhd2/93K6enF1dOl1b/H/7vHBMrFm11HtmcbqKBhjaPVwUvERERERERERHp\nqCYC9/p8vh4+n6+3z+frCdwFPN+ePzQjI5OAv3mPqFgstkv7bhUWl/DIC29x1LiTefHJB3c+oY0Y\nTg+xcIvVAJaFYcTzMBsLXImhUADTlUHGwIMwHB42vXUPgWUzcRb1wjBMTHcWnt7xT+y7e49otVqs\nvdUFImS57InrhmEk3lutDYTJdjsSY5luO9UNIS44sj+T525gzC3/4Yg7P+LRc/fDYTO44sUZ/O7Y\nAbx+xUGU1wSpqAtt/ePajd3tIRJs/p1jxXa6N9KO5qyd9Tmz//EXDjj/NpyZOe0R8jbVNoTJ9jT/\nzs0Wj0dNQ6jVWLbHQXV9iMtOGMpHs9Yw/NI3OeDKt5j4u0Nx2k221Ab59/RVAPx7+ir26pu8FXcQ\nP9f9/uY33S3L+knn+tJFC1i/djV/efgeHrjrBtasXM5zjz+w84ltxO7OIBxocT5b1g94bm1/TmZB\nMcff+jT9xhzLzH880z5Bb0N9MEJmq3O9uY5SH4gXuJpkuuzU+sO8NWMtDcEIL1+wH0cMKmbe2prE\nMccOK+HWEwdywfPfUtWQnP27AJzuDEKtfrc7P9e3N6fPXgdS3KsfAH33Hk35qqXbu4t2YXN5iIS2\n+n9kJ7lse46NRPUSiIYC2N2Z25i9a9K34KWWhiIiIiIiIiIiuzu3z+eb1vIGn883tb1/qHfwcGZ+\n8yUAi+Z/T889+v2o+S1Xd0y49Uo2rF0NgDsjE9OWvLfjXN36E1gZbxkV3LAER5fmdmb2gu5EqjcR\nC9ZjRSME1y/CWdKP0KZluMoGUnzyjXj6jcSWE1854eq+J4GVs+P3tW4R9i6l//sD28k3SzZzxNAS\nAPbuU8DCtdWJscXra+ldnEWOx4HDZnBA/0JmLKugqiFEbePKr5qGMHbTwGYaHDm0GxdNnMZpD31O\nQZaTKfM3bvNntoeCPQayccEMACpWLCSnW++fPGf1jMks/3ISYy75AxkFxe0V8jZ9vXAjR+8Tb8U2\ncs8i5q6sSIwtXFNF32455GY4cdhNRg8sYZpvI5V1QWrq48XFyroQdtPANA2+WrCBY/btCcCYwd1Y\nsKoyqbkMGDKcb6d+AYBv/hx6/cRzvf+AwTzy3Bvc9eDTXHXLvZT17sO5l1zV5vFuT1GfQaybH3+e\nbF6+kLxuvX7ynM+evova8nUAOFyenRY32tLMFZUcPCBe9BxelsuiDXWJsaXl9fTqkkG2247DZrBP\n73y+W1XF0B65fL1kC2c9NZ3/zN3Imop40eiEEd04c1QZZz89nXVVyW0D2K3/YFbOie9PtX7pAgp7\n7PGT5/zzwRvZuHwRAKsXzKK4V/92inrbcnoOoHLRTABqVi8io7jnT56T1b0P1SviqzsrFs8kp9fA\nNo/XvvNDOiat8BIRERERERGR3UE0FiMUjhEKRwlHY0RjFtGoFf8ei18vKspOdZipMtvr9T4HfABU\nA9nAcUDbb/zRwsgxhzFn5jRu+d25AFx0zW188ckHBAMBjjjuxOYDW3xavSWjxe0nnn4Oj//xdhwO\nJ063mwuvvLk9Q2/F3WcfAqvnsenNuwHIP+I8GhZNxQoHyRx8CLljTqf8nfieY5mDDsKWmQemnYpp\nT1I74z1MVwb5h8d/B7mjT6fyk+eomzsZ0+mhYOyFScvj37PWccigrvzrukMB+P0LMzhxZBkZTht/\n+2IFt78xm9evOAjDgL99sYJN1QGe/mgxD/1mX96+5hAcNpN73ppLIBxj+aY63rzqYBqCUb70lSf2\nBkuGbkNHUb7oOz7/87UA7HX671gzcwrRUJBeB4xtPtDYwZwzfo8Vi/H9PyeSkV/EN8/dCwZ06TuE\nAUefkZQ83pm6gsOHl/LJPScA8H+PTeHUMX3IcDt44b8+rnt+Gu/dfgwGBi987GNDpZ9H353LU5ce\nzEd3j8NhN7n1lRkEQlFueGEaT1xyEOcfPYDq+hC/eWhyUnJocsBBhzP722nccOk5AFx63e18/vEH\nBAJ+jhp3UvOB2z7VW53rqdRj+Cg2+Gbx0YPx/cP2/9XvWTFjCtFQgL4HHt3iSGOHcwAGHfULpr7y\nMDa7HZvTxcgzLk9aHh/N28SB/bvwtwtHAnDjm3MZN7wEj9PGm9PXct97C3n2t/tiAG/OWEN5bYhw\n1OLyscO48PA+VPsj3PzmXAwDbjphAGurAjx21l5YWExfVsnjHydndVTfvUezet5M/n7PFQAcee5V\n+KZOJhwKMOTgYxPHGS0ej23NATj87Mv59JXHsdntZOTmc/ivf5+UHJp0Gbg/lUtnM/uZGwHof9Kl\nbJrzObFwkJJ9jmw+sMW5sK05AH2OPpvF7/wFKxrBU9SDwsGj2jxeI9n9RNvCCVe9Yw3r24Xf/yK5\nPV07iqKibMrLa1MdRkp05tyhc+ev3Dtn7tC58+/MuQMUFWV3jL8c0ofVWZ8vOlc6b/7KvXPmDp07\n/86cO/y0/CPRxmJSJEooHCXYWFiKxiwsyyJm0fjdwrJoUWyKEY1aRBoLTrHGL8sicWzMit8WjjTd\nf/y+g5HG4lUk1ly0aixgRRoLWqFw/PhgYyw78+4D4zvlayOv12sAJwJjgBygBvgSeNvn8/2gN7Vm\nr6pNvze/tjK8Z7zgecSjX6c4kl3z8WXxNzdLzn8zxZHsug3PnMK1k3ypDmOXTRjnxXPSxFSHscv8\nb58HwPx19Ts5smMb1D3e4u32DxenOJJdd/vY/gy4/j+pDmOXLbzvaB7/ckWqw9hll4zuzXmvz011\nGLts4mlDYLslaK3wEhEREREREZEOpuWKp6biUTAcZU2Fnw2bamkIRmgIRPAHIzQE49+bvpquNwQi\nBEI/rJjU3myNrdRsNgObaWKzGbjsNrI8TlwOE6fDhtMe/263xcfj7dfMxjmdstYFQGNR6+3GLxER\nEZHtSt+Cl/bwEhERERERESEWs6gPhLEA0zAwDQPDANM0MA2IxSAcjRGOxAhHY0Qi8cuRROu8+PdI\ni5VNTeOt5kRjxLbzp3gsZjUXmrYqQDXHFW/9ZBoGhmlgWS1WUrVo4dcU10/lctrIcNnJzXJR4rTh\nctpw2m04GwtLLrsNh8PEZrb+XRmNMZpNhaZEgcqIF6ESxzfm0vj7NQ0Dh7110crpsOFymIl5HaXl\nlYiIiMjuLC0LXoahFV4iIiIiIiKye7AsC38wQmVdiOq6INV1IfyhSLzYtFXhyTBNNlc2UOsPU9sQ\npq4hREMgXlTqSGymgcdlx+OyYRjx4lYsFm8BGInGiFnxv+3tpoHTbksUlmymid1mtCoeuRzNxarC\n/AxikSgel50Ml73xZ9jxuJuu27CZZqrTFxEREZEUSMuCl800VfASERERERGRdmNZ8X2ZmlcsRRMr\nlpr2bUrs4dRq76emPZuaL4ejze35Qo3t+ULhKIFwlJr6EFV1IcKRH9fFxDAg2+MgN8tFaVEWWR4H\nhkE8ppjVao+pphVIdlv8u8Nui1+2mYkWe3Zb61VNDpuJ3W42f2+8bG6ntZ5pGHhctkQRymE322VV\nU2ffw0xEREREti8tC152m0E0qoKXiIiIiIhIe7AsK7H3UaINndncki5+DI1FlfjKHYt4ESgadL3x\n5wAAIABJREFU206bulgscTkajSXa51mW1dhOjuZ2cWb8ssthw+204XbacTnjl+22Ha/esaz4z9i6\nfV9DMEK9P0xdIEy9P0JDIEx9oPU+UFu34ou089+dpmGQk+mge2Em+VkucrOc5GY6yctykeG2J4pM\nDntz8amkaw4hf4gMtx1TbfJERERERBLSsuBls5lEt9c4XEREREREpJOLt8iLUlkboLI2SEVtkPpA\nuLng1KL4FI7GqG9sj1fbEKbOH6LOH273Ys9P1bRKaVtNP6zGQtdPjdxpN/G47GR5HBTleRLt8jJc\ntkQLPbfLjr1pvyfTwIDmgp1pYDebV03ZbAZ208BmM5vb8tnjezs5HbaftLdTUVGWVjiJiIiIiGxD\nWha87DZDLQ1FRERERCTtRRpb3YEVL6A0rm5quhyzLELhKMFwjFAkmmiLt7bSz9oNNY1FqniBqq6x\naFVVFy9wBUPRHx2Px2Uj2+OkZ1c3WR4Hdpv5P+3xrMbLLVdlxVdmxS+33Itp25fjBaCmFnot2/A1\nrRJrag8YDEcJhqIEQhECoWjiy7QZRML/+yFIw6CxdV9jC74WLfky3XYy3A6y3HYyPQ4y3Q4y3HYy\n3c37QO1s9ZiIiIiIiHRcaVnw0h5eIiIiIiKSDDEr3n4v3h7PIhyJEo7EiEStxu+NYy3a5wVCEeoD\nEeobW+fVJ1rnhQmFYwRbFK7a4++aLI+D4jwP+dkuCrJd5GW7yM92ke1xNhaZ4gWnpr2a7KZJpsdB\ndoYjbQo+2sdJRERERES2lp4FL+3hJSIiIiIiP1AoHGVzdYAtNQH8wQjBcHPBKRiOEorECAQj1AXi\nezw1BJqLVP5gpE1iMAC3yx7fj8phIyfD2aK9nQ2ItyGMtdoXK77qK9EKz2HDZY9fzs/LwLRiZHkc\nZHscZGU4yc5wJFZliYiIiIiIdDZpWfCymyb+cDjVYYiIiIiISApZlkVDIEx1fYjahjA19SGq60NU\n1wfZXBWgvNrP5qoA1fWhH3W/LoeNTI+dLjluMtx2nI2t8Vq2yWu6nLitxZjTbpLlcZDpaWqZ5yDD\nZcc0f9xeTTuiFU4iIiIiIiKtpWXBy6Y9vEREREREdjuxmEV1fYjK2mDjV4DKuiD1/vhKK38wQkOL\n7w2BCOHI/+7j1MRmGhTkuBjYK5/CXDeFufF9qZwOW/zLbjZeNnE77WQ17vHksGuFlIiIiIiISLpJ\ny4KX3WaqpaGIiIiISAcWjcXi+1WFo4QaWwgGI1Hq/WGq6kJU1wWpqgtRVRekuj7+vao2RMza8et8\nm2mQ4bbjcdnpWpBBhtNOTqaD7AwnOZlOchu/uuS6yc92YTNVvBIREREREekM0rLgZbMZRHfyh7CI\niIiIiLSPmGURCEap84fYVOVnU2XzV3lV/Cu0g5VXW7OZBrlZTvp0zyEv20VBtov8Fl9ZnnhLQI/L\njsNuYhjx1oBq6yciIiIiIiJN0rLgZTe1wktEREREpC20bCNY2xDfC6vOH6bW33i5IUx9INyipWCU\nQDDC9l6Ne1w2SgoyyPQ4cNpNXE4bTnu8baDTYSPTbSc300VelpO8LBe5WU4yPQ5Mo+32txIRERER\nEZHOJy0LXqZpEI398E+MioiIiIh0NuFIjNqGEDUNIWrqQ1TXxwtY1XWh+N5YtUEqaoNU1+28jaAB\nuF12Mlw2uuS4yXDZ8LjsZHocFOV5KM73UJznoSjfQ7bHkViBJSIiIiIiIpIsaVnwsttMLCveSkWf\nBBURERGRziJmWVTXhaioCbClJkBFTZCIBZu21FPbEGpcmRWmtiG+ImtHbKZBXpaLPqU5FGS74qut\nMp1keRxkZcT3xMpuvOxx2fW6W0RERERERDq0tCx42WzxP7ajUQvTrj+8RURERGT3EY7EKK/ys7Gi\ngQ2VDWysaGBTpT9R4IrGtr8ay2YaZHkcFOS4yPZkkZPpJCfTSW6mk5wMZ+J6QbaL7EynilgiIiIi\nIiKy20jLgpfdZgIQjcVwYKY4GhERERGRH8ayLOoDESpq4i0FW38F2FTlZ3N1gG11GMzNctKrJJuC\nHDeFOW4Kclx0yXHTs0cekWCYbI8Tj8umdoIiIiIiIiLSKaVlwctmxv+Ij+3g060iIiIiIqkQjcVY\nsb6WFRtqqWjcK6uqcb+sytog4cj296LNyXDQvzSXrgUZlBRk0LXxqzjPjcNu2+acoqJsystr2ysd\nERERERERkbSQlgWvphVeERW8RERERCTFLMtiY6Wf+SsqmLe8goWrqv5n/ywDyMl0UlqYSX62i4Js\nN3nZTgqy3eRnu8jPcZGf5cLp2HZRS0RERERERER2LC0LXk0rvKJRFbxEREREJPmCoSjzV1Qwe+lm\n5i2vYEtNMDFWlOdm/4HF9C/LozA3XtDKy3IlPrQlIiIiyTW8Z3aqQ2gzH182KtUhtIkNz5yS6hDa\nxIRx3lSH0Cb8b5+X6hDazKDumakOoU3cPrZ/qkNoEwvvOzrVIbSJS0b3TnUIbWLiaUNSHUK7S8+C\nl62x4BXbfjsYEREREZG2tLnKz+ylW5i9dDMLV1YRicZfi2a67ew7oJhBvfMZ1LuA4jxPiiMVERGR\nlioboqkOYZflZ8RXgU/2bUlxJLvmMG8XANZUhlIcya7rke/k1VlrUx3GLjtjr1L2uGJSqsPYZcsf\nGgfAuKe+SXEku2bSBSMB8Jw0McWR7Dr/2+eRc/pLqQ5jl9W8djZHPPp1qsPYZR9fNoqyS95JdRi7\nbPXj43c4npKCl9fr3R+4z+fzHeb1eouAZ4A8wAac7fP5lu9oftOnY6NqaSgiIiIibSxmWWyu8rO2\nvJ61m+tZt7melRtrWb+lIXFMWXEWw/t1YXjfQvboloPZ2IFARERERERERFIj6QUvr9d7DXAWUNd4\n0wTgFZ/P96bX6z0UGADssOBlayp4qaWhiIiIiLSB1ZvqmDpvA/NXVLJ+Sz2hSOtOAk6HybC+XRje\nr5DhfbtQkONOUaQiIiIiIiIisi2pWOG1BDgJeLnx+mhgttfr/Yh4oet3O7sDe9MeXlrhJSIiIiI/\n0ZbqANMWbOTreRtYW14PgMNu0q1LBqWFmXQvzKS0MIvuRZkU5roxDa3iEhEREREREemokl7w8vl8\nb3u93l4tbuoNVPh8vqO8Xu8twPXAbTu6j6YVXjEVvERERETkR4hEY0ydt5Evvl/PotVVANhtBnvv\nWcQBg7oyvF8XHHZbiqMUERERERERkR8rJXt4bWUL8G7j5XeBu3c2wW6Lf7o2O8dNUVF2+0XWgXXW\nvKFz5w6dO3/l3nl15vw7c+4ibSkSjfHF9+uZ9NVKttQEAPCW5XHA4K7sO6CYTLcjxRGKiIiIiIiI\nyK7oCAWvz4HjgL8CBwPzdjahaVPwzVvqKcjofG9OFBVlU15em+owUqIz5w6dO3/l3jlzh86df2fO\nHVTsk7YRjsQLXf/+egVbaoI47CZH7tODsSPLKMz1pDo8EREREREREWkjHaHgdTUw0ev1XgRUA2fu\nbIK9saWh9vASERERkW0JR6J8Nns9/566ksraIE67ydj9yjhm/57kZblSHZ6IiIiIiIiItLGUFLx8\nPt9K4MDGy6uAsT9mvq2xpWE0Fmvz2ERERESSyev1/hr4DWABHmA4cBDwMBAD5vp8vktSFmCa8Qcj\nfDprLf+Zvpqa+hBOh8nRI8s4ZmRPclXoEhEREREREdltdYQVXj+a3Wxc4RXVCi8RERFJbz6f70Xg\nRQCv1/sY8CxwK3Cjz+f73Ov1Pun1esf7fL53UhlnR1fbEOKjGWv45Ns1NAQjeFw2jjugF2P3KyMn\n05nq8ERERERERESknaVlwcvW2NIwppaGIiIispvwer37AoN8Pt+lXq/3dp/P93nj0PvAUYAKXttQ\nWRvkn1+t4IOvVxAKx8jyODj54D4cvncpGe7Ot9eriIiIiIiISGeVlgUve6KloQpeIiIistu4Abh9\nG7fXArnJDaXjq6wN8u+pK5ny3VoiUYuCHBfHHNKTg4Z3x+WwpTo8EREREREREUmytCx42cx4wSui\nPbxERERkN+D1enOBPX0+32eNN7V8kZMNVP2Q+ykqym7r0DqcypoAb36yOL6iKxKjuCCD047ck8P2\nKcNhN1MdXsp0hsd+e5R759WZ8+/MuYPyFxEREZFtS8+Cl017eImIiMhu5WDg4xbXZ3m93oMbC2DH\nAp/8kDspL69tj9g6hOr6EO9PXcmns9YSisTokuPihNF7cOCQErqV5O7Wue9MUVF2p81fuXfO3KFz\n59+Zc4fOnb8KfSIiIiI7lpYFL7U0FBERkd2MF1jW4vrVwDNer9cBLADeTElUHYBlWXw+Zz2vfbyY\nQChKQY6L40f1ZsywbthtnXdFl4iIiIiIiIi0lpYFL5vZuMJLBS8RERHZDfh8vj9tdX0xcGhqouk4\ntlQHeOH9BcxbUYnHZePMI/tzyIjSTt26UERERERERES2LS0LXk2f5o2p4CUiIiKy27EsiynfreP1\nyUsIhqIM7dOFXx/jpSDHnerQRERERERERKSDSsuCl62ppWE0tpMjRURERCSdbK7y8/z7C1mwshKP\ny865xw1k9NASDMNIdWgiIiIiIiIi0oGlZ8HL1B5eIiIiIrubbxZs5Pn3FxIMRRnetwtnHzOA/GxX\nqsMSERERERERkTSQngWvxpaGERW8RERERNJeLGbx9ufLmPT1SlxOG+cdP5BRg7WqS0RERERERER+\nuLQseNnV0lBERERkt9AQiPD0u/OYs3QLxfkeLvv5MEoLM1MdloiIiIiIiIikmbQseNnM+AovtTQU\nERERSV/rt9Tz6D++Z0NFA4P3KODC8YPJdDtSHZaIiIgIlmUx4Z47WbLIh9Pl4sZb76S0R1mrYwJ+\nP5dffD433343PXv1Ttw+9/vZPPHnh3jimRcA8C2cz/1/uAOX00V/7wCuvPbGpObx6pN/Ys2KxTgc\nTn512Q0UlZS2OiYUDPDIrb/n7MtvpGtpz8Tty33zePulJ7nyD48BUL5+DS8+8gcMw6B7rz6cceHV\nSc3jkQl3s3SJD6fTxVU33k730q0ej4Cf6y6/gKtvvpOynr0Tty+YO4dnnniYB594DoC7b7mWyoot\nWJbFxvXrGDR0GDfdOSFpeUx69mE2rFyK3enkZ/93NQVdu7c6JhQM8PI91zL+gmso7F623Tlv/vku\n6qorwYKq8g306D+IUy6/OSl5ANx1yhAGds8hGIly/etzWL3Fnxg7YnAxlx3Vn3AsxpvT1vD6tNU4\nbAYTzhhOzy4Z1PrD3PqPeaza0pCYc9P4gSzbVMerX69OWg4Qf0zKP36BUPkqDLuD4qPOw5FXnBiv\nXzqTiqnvYNhsZA8+mNyhh2JFI2z8z9OEq8uxOT0UHfFrHHld2TDpcaIN1WBBuKYcd7f+lIy7OGm5\nPHLBaIb1LiAQjnLR45+zYmNtYuy4fXtyw6kjCEcsXvpkES/814fNNJh4+SH0Ks4mEotx8ROfs2Rd\nDYU5bp64eAy5mS5spsFvH/mUlZvqkpbHg7/dn6G98gmEolz29NesaPGzj9m7B9edPIxwNMYrny7h\npclLsJkGT108mp5FWUSiMS5/5muWrK/lucsOoijXjWEY9CzKZPrizfz20c+TkoNlWVRNeYnw5tUY\nNgf5h5+DPbf5eeVfPoua6f/CMG1kDjyIzMGHYEUjVHw8kWh1OYbLQ/4hZ2PPLSbqr6Hyk+eJBRvA\nsig46nzsOUVJyaPJPacPY2BpLsFwlGv/+l2rc/fIIV353bFewtEYb0xdxWtfrcJhM3jgrL3o2SWT\n2kCYm1+fw8rNDfQqzODBs/YmZln41tVy8xtz2jxWs83vMQmaVnjFVPASERERSUtzlm7m7pdmsKGi\ngWNG9uT3vximYpeIiIh0GFMmf0w4HOKZF//GRZf9nkceuL/V+ML587jovF+zbk3rN+dfefFZ7r3r\nNsKhUOK2++66nSuvvZEnn32JrKxs/vP+e0nJAeC7qZ8RCYe4dsLTnHj2Rbz57J9bja9cspAHbriY\nzRvXtbr9w7f+yiuP3Uck3JzH35/9M+PPuoCr7n0CK2bx3dTPkpIDwJdTPiEcDvHoM69w3kW/48lH\n/thqfNHCeVx50TmsX7em1e2vv/I8D9x7O+EWedx81wQeePxZ7rz/YbKyc7j499clJQeAhdO/IBIJ\nc95dj3Hk6efzn5efbDW+btkiXrjjCio3rd/pnFMuv4Xf3PIgp111J+7MLI759SVJy2Ps0K447San\n/Pkr/viej5vHD0qM2UyDm8YP4ldPTuOMx6ZyxqieFGQ6Of2AntQHI/z8ka+44+353PnzwQDkZzp4\n7vz9OGJw16TF31L9km+xohF6nHEbXcacyuYpf02MWbEom6f8jdJTrqf0FzdSM2cy0YYaqr+fjOn0\nUHbGbRQefhblH78IQMm4Syj9xY2U/Ox32NyZFB72y6Tl8bP9e+FymBx2w7vc+vJ0Jpyzf2LMZhrc\nf87+HHfb+4y95T1+O9ZLYY6bY/Ypw2YaHH7ju9z7xizu/OV+APzh7JG8OmUJR98yiTv+9i3eHnlJ\ny+P4/cpw2W0cdesH3PHaLO45a99Wedx71r787A8fcdyd/+GcI/akS7aLsXuVYjMNxt72ARPe+p5b\nT98bgHMf/ZwT7v6IXz4wmar6ENe9OD1peQSWzcSKRig+5WZyRp1C1RevJsasWJTqL16j6MRrKTrp\neurmfUrUX0P9vE8xHW6Kf3ELeQf9isopLwFQ/eUbZHgPpPjkG8jZ/yTCleu392PbxTHDu+G0m5z0\nwOfc968F3PrzIYkxm2lw68+HcMajX3Hqw1/yy9G9Kchycubo3tQFIpz4wOfc+vfvufu0YQDc+vMh\n3P+v+fzi4S8xTRg7rKTN403LglfTHl5a4SUiIiKSfj6asZpH/j6HcMTi/OMHcerh/RIr+EVEREQ6\ngtmzvuWAA8cAMGTocBbMn9dqPBwOc/9Dj9Jrjz6tbu9R1ov7H2hdVNq0aQNDhg4HYOjwEcyeNbMd\nI29t6fzZDNrnAAD28A5m5ZKFrcajkTAX3nQ/JS1WdgEUdevBhTfe2+q2VUt99B88AoDB+xzAwtkz\n2jHy1r6fPZP9DhgNwMAhw1i04H8fjzvuf4SyXnu0ur20R0/uvP+Rbd7nC888wUmnnkF+QZf2CXob\nVvnm0m/4SAB69B/IumW+VuPRSJjTr76Twu5lP3jOp39/gf2POYms3Px2jr7ZfnsU8NnCcgC+W1XF\n0LLcxFi/rlmsKK+nLhghErOYvqyC/fsV0L8kiykL4nOWl9fTr2sWABlOOw9/sIi3Z6z53x+UBIF1\ni8joHX8z3t2tH4GNyxNjoS3rcOR1xXR5MGx2PD28+NcsJLxlbWKOM78boYrWBeOKr98id8RY7Bm5\nJMuBA0v4aGb8dzh9cTl7921eATSgRx5L1tdQ6w8TiVp8tWAjYwaVsHhdNfbG99lzM5yEIlEARg3s\nSmmXTN677VhOO7gvn81NXoFllLeY/85eC8CMJZvZq0/z+ektzWXphuY8vvZtYvTArixZX5OoF+Rk\nOAhHWm+DdOMvRvDUBwvZXBNIWh7B9Ytw9xwKgKukL+FNKxJjkYp12PO6YjrjzytX9z0JrvURrlyH\nu1f8eeXILyHSWNgKrV9MtK6C8nf+iH/RVNylA5KWB8B+fQv4dP4mAL5bUcmwns0F0P4lWSwvr6cu\nED/fv1m6hQP6d6F/SXZizvJN9fTtmg3A0LI8vllaAcDkeZsY4237lWpp+c6CzWzaw0sFLxEREZF0\n8uE3q3j1v4vJyXJyw6/2ZtSQtv9El4iIiMiuqq+vJysrO3HdZrMRizW/iTp0+AiKi7tiWa3fmzr0\n8COx2Wytbivt0ZPvZsaLQ1989ikBv59k8fvr8WQ0749qbpVHnwFDye9SxNbvsO016hDMrfJoye3J\nwN+QvBZnDfX1ZLZ6POyt8hg8dARF23g8xhx6xP88HgBVlRV89+00jh53YvsFvQ1Bfz3ulo+H2frx\nKNtzMDkFRbR8QHY0p76miuXzZjHikGPaP/gWstx2av3hxPVozMIwWowFmsfqQxGyXHbmranh8EHx\nlm4jeuVRnOsGYG2lnzmrqzGa7iDJYkE/psuTuG6YNiwr/vuNhfyYrozmMYeLWMiPs7g3Dcu+AyCw\nbgmR+qrEcy/aUIN/1XyyBx+UxCwgO8NBdUPzSsZILJZ4THIynNS0GKv1h8nJdFIfCNOrazazHzuF\nRy8awxOT4oXkXkVZVNQFOf6O91mzuY6rTx6evDw8Tmoamp8/kRbPrWyPo9VYnT9MToaT+kCEXkVZ\nfPvgeB457wD+8v6CxDFdsl0cPLiEv05ZmrQcAKyQH9PZ/LzCNJufV+EARosxw+HGCvlxFvYisCL+\nvApuWEK0vgrLihGp3YzpzqJo/DXYsguo+XZSUnPZ+nyPtDrfHa3G6oMRst0O5q6p4ogh8VWbe/XO\npyTPjWFAy9O8LhAh29P2XV7SsuBlT6zwiu3kSBERERHpKD78ZhWvfbKEvCwn15+5N3t0y0l1SCIi\nIiLblJmZSUN9feK6ZcUwf+KK9Jtvv5sXnn2ayy78LQUFXcjNS157MI8nk4C/ea8VK2b95DwMo3le\nwN9ARmbWLsf3Q2VkZtLQ0DaPB8Bnn3zE4WPHJb3I4vJkEmz5eFg7fzx2NGf+1CkMHX1E0vOoC0TI\ndNsT1w3DoKnWWBeIkNWiVXmmy06NP8Lfv1lNXTDC65cewFFDujJ3dXVSY94e0+XBCrVY+WNZiee6\n6fQQCzUXqGOhAKYrg5zBB2M43ax5/W7qln6Lq7h34jGoW/wNWQNHJf0xqW0ItyoemC0ek5qGUKux\nbI+D6voQl50wlI9mrWH4pW9ywJVvMfF3h+K0m2ypDfLv6asA+Pf0VezVtzB5efhDZHman1st86j1\nt84xqzGPS44byH9nr2WfK9/hwOve5alLxuBorB+ceEAv/v7lcpLNcHqIhbfzvGoscCWGGp9XGQMP\nwnB42PTWPQSWzcRZ1AvDMDHdWXh6x1fXunuPaLVaLBm2Pt9Ngxbne5isFmNZLjvVDWHe+HoV9YEI\nb14xmqOHd+P7VVVYFrRs2JfltlPToljWVtKy4GVr3MMropaGIiIiImmhZbHrujP3pmtBxs4niYiI\niABer3ey1+v9aquvr71e71ft9TOHjdibr76M71E1d85s+vbb80fNb7nS6MvPp3DnvX/k0b88S1VV\nJSMPOLBNY92RvgOHMW/G1wAsWziX0t59djKjtZZ5lPXpz+K5swCY9+1U+g0a0XaB7sSQYXvxzVef\nAzB/7mz26Nv/R83feuXXzOlTGTlqTJvF90OVeQez+LtpAKxePJ+uPffYyYwdz1k2dyb9Roxsn2B3\nYMbySg4d2Lxay7e+NjG2ZGMdvQszyHbbcdgM9utTwMyV8TZoXy3ewmmPTeX92etZtaVhe3efVO7u\n/alfPhuIr9ZyFvZIjDm7dCdctZFooB4rGiGw1oe7W3+CG5aR0XMwPU67maw9R+LILU7MaVg5j8ze\nyVsR1eTrhRs5ep94K8yRexYxd2VFYmzhmir6dsshN8OJw24yemAJ03wbqawLUlMfX/lVWRfCbhqY\npsFXCzZwzL7xNqdjBndjwarKpOUx1VfO2BHxx2C/foXMb/GzfWur6VOSTW6GA4fN5MABxXyzuJyq\n+lBi5Vd1QxibaSQ6xB06pBsffbc2afE3cXXrT2DlHCC+WsvRpfl5ZS/oTqR6E7Fg/HkVXL8IZ0k/\nQpuW4SobSPHJN+LpNxJbTvx55eq+J4GV8edocN0i7F1Kk5rL9KUVHD64ebXWwnU1ibHFG+roXZRF\njid+vo/s14WZyysY3iufL3ybOeWhL5k0cy2rNsfP97mrq9i/X7xN5WGDi/lmyZY2j9e+80M6Hnvj\npxjU0lBERESk41OxS0RERHbR9cAzwElAJBk/8NDDj+SbqV9x/m9+CcAtd/yBD9+fhN/vZ/zJpySO\n294qjpa3l/XsxaX/dw5uTwb77DuSUaOT1+psxKhDWPDddCZcewEAv/7dTUyf8iHBYIAxY3/WHO92\n5rfM4+fnXsYrj91HNBKhW1lv9h59WHuG3sqYQ4/g22++5vLzzwLgmlvu4pMP/43f72fc+J9vM96W\ntr59zeoVdCvtsc1j29PA/Q5i2ZxvefbWywAYf+G1fP/lx4SCAfY5fFzzgcaO5zTZsn4N+cXdkhJ7\nS//5fgNjvIX8/fJRAFz76hxO2Ks7GU4br09bzd3vLODlC/cHA96YtprymiDhSIyrjt2TS47sR7U/\nzHWvzWl1n1sXJZMls9++NKycy5rX7gSg+OjzqV34NbFwkNyhh1J4yC9Z948JgEXOkEOwZ+Vh2Gxs\nmfQmldPewXRnUjz2vMT9hSs3tCqAJcs7U1dw+PBSPrnnBAD+77EpnDqmDxluBy/818d1z0/jvduP\nwcDghY99bKj08+i7c3nq0oP56O5xOOwmt74yg0Aoyg0vTOOJSw7i/KMHUF0f4jcPTU5aHu9OX8Vh\nw7rx4R3xNp0X/+VLTjmwNxkuOy9NXsKNL8/gnzcehWHAS5OXsLHKz+P/XsDjFx7I+7cdjcNmcsdr\nswiE4/uR9euWw4pNyWu/2sTdZx8Cq+ex6c27Acg/4jwaFk3FCgfJHHwIuWNOp/ydPwGQOeggbJl5\nYNqpmPYktTPew3RlkH/4uQDkjj6dyk+eo27uZEynh4KxFyY1lw9mr+fggUW8dWX8QwJXvTKL8fuU\n4nHZeO2rVdz5j7n89dIDMQx47atVbKoJEorGuPr4fbnsmD2pbghxzV/jrRrvemseE84cgd1msGRD\nHZNmrdvRj/5JjFT9Y7IrtlT7rd/c+SEjBxZz4fghqQ4n6YqKsikvr935gbuhzpw7dO78lXvnzB06\nd/6dOXeAoqLs1DRxT19WR3y+JKPYpXOl8+av3Dtn7tC58+/MuUPnzr+zvzbyer3XAEu9iXsIAAAg\nAElEQVR8Pt/bP3ZuZUP6f2I6PyO+D9VkX9t/Gj6ZDvPGP9m/pjK0kyM7vh75Tl6dlfyVI23tjL1K\n2eOK5O4J1B6WPxQvGI576psUR7JrJl0QX7HnOWliiiPZdf63zyPn9JdSHcYuq3ntbI549OtUh7HL\nPr5sFGWXvJPqMHbZ6sfHw/Y/p5GmK7wae3DG1NJQREREpMPSyi4RERFpKz6f74+pjkFEREQ6tjTd\nw6uxpaEKXiIiIiId0jcLNqrYJSIiIiIiIiJJk54Fr8ZN51TwEhEREel4lq+v4dlJC3A7bVx12ggV\nu0RERERERESk3aVlwctuayx4RWMpjkREREREWqqoCfDnf8whEo1x4fjBlBZlpTokEREREREREekE\n0rLgZTPV0lBERESkowmGojz6j++prgtx2mH9GNa3MNUhiYiIiIiIiEgnkZYFL9M0MAyIqOAlIiIi\n0iHELItnJ81n5cZaDh7ejaP2K0t1SCIiIiIiIiLSiaRlwQviq7xiKniJiIiIdAjvfL6cGb5yvGV5\n/GqsF8MwUh2SiIiIiIiIiHQi6VvwshlEoyp4iYiIiKTa1PkbePerFRTlubn4pCHYbWn7ElNERERE\nRERE0lTavhthMwyisViqwxARERHp1FZvquO5SQvxuGxcfspwsjOcqQ5JRERERERERDqh9C142Qyi\namkoIiIiklJ/n7yESDTGeccPorQwM9XhiIiIiIiIiEgnlb4FL1MtDUVERERSybeqkrnLKxjYK5+9\n+helOhwRERERERER6cTSuOBlqqWhiIiISIpYlsWbU5YCcPIhfVIcjYiIiIiIiIh0dulb8FJLQxER\nEZGUmb10C0vX1rBX/0L6ds9NdTgiIiIiIiIi0smlb8HLVMFLREREJBVilsVbU5ZhACcfrNVdIiIi\nIiIiIpJ66V3w0h5eIiIiIkn3zfyNrCmvY9SQEkqLslIdjoiIiIiIiIhIOhe8TK3wEhEREUmySDTG\nPz9fjs00GD9mj1SHIyIiIiIiIiICpHPBy2YQjcVSHYaIiIhIp/LFnPVsqvJzyIjuFOV5Uh2OiIiI\niIiIiAiQzgUvtTQUERERSapQOMq/vlyO025ywoG9Ux2OiIiIiIiIiEhCWhe8LOKbpouIiIhI+/tk\n5lqq6kIcuW8ZuVmuVIcjIiIiIiIiIpKQvgUvWzx0rfISERERaX8NgQiTvl5BhsvOsQf0THU4IiIi\nIiIiIiKt2FMdwE9lMw0AorEYjvSt24mIiIikhQ+nr6I+EOHnh/Qh0+1IdTgiIiIiP1h+hi3VIbSZ\nw7xdUh1Cm+iR70x1CG3ijL1KUx1Cm1j+0LhUh9BmJl0wMtUhtAn/2+elOoQ2UfPa2akOoU18fNmo\nVIfQJlY/Pj7VIbS7tK0UNRe8tMJLREREpD1ZlsUX36/H47Jz5D5lqQ5HREREREREROR/pP8KL7U0\nFBEREWlXy9fXUlETZNTgElzO3ecT0iIiItI5nPf63FSHsMsmnjYEgEAkxYHsInfjO5HjnvomtYG0\ngUkXjKTskndSHcYuW/34+N3m8QC4/cPFKY5k19w+tj8Ae1wxKcWR7LrlD41jwPX/SXUYu2zhfUfz\n6qy1qQ5jl52xVynXTvKlOoxdNmGcd4fj6bvCq2kPL63wEhEREWlX3/o2AbDvgKIURyIiIiIiIiIi\nsm3pW/BKrPCKpTgSERERkd2XZVnM8G3C5bQxZI+CVIcjIiIiIiIiIrJN6V/wsrTCS0RERKS9rNpY\nR3nV/7N33/FRVekfxz9T0iYJkIQECL0eetEVLKiADZdV3LUg1tW14KLYu2J3XXRtP7voqmvvurJW\nZLEjRelcWoCQUEJIIEwyybTfH5PKEggmmWEy3/frta8l99xz8zxkJoz3uec5Hob0zCDOqXaGIiIi\nIiIiInJgit6CV1VLQ+3hJSIiItJs5q+sbGdosiIciYiIiIiIiIhI/aK34GWrXOGlPbxEREREmkUw\nGGTuigLinXYG9ciIdDgiIiIiIiIiIvWKSMHLGDPCGDNrt2NnGWN+aOg1HI6qgpf28BIRERFpDnnb\n3GzZXsqgnhkkxKudoYiIiIiIiIgcuJzh/obGmOuBc4FdtY4NAy7cn+tU7+GlloYiIiIizWK+VQCo\nnaGIiIiIiIiIHPgiscJrNfDHqi+MMRnAvcCV+3ORmhVeKniJiIiINId51lacDjuDe6qdoYiIiIiI\niIgc2MJe8LIs6wPAB2CMsQPTgWsAN2Br6HUc9lDoKniJiIiINL1NhW7yCtwM7J5OUkLYmwKIiIiI\niIiIiOyXSN+9OAjoBTwNJAH9jDEPW5Z1zb4mtkpNBCAlNZHMzNRmDfJAFIs5V4nl3CG281fusSuW\n84/l3CWyqtsZ9s2McCQiIiIiIiIiIvsWyYKXzbKsecAgAGNMV+CNhhS7ADxlFQBs3+6moKCk2YI8\nEGVmpsZczlViOXeI7fyVe2zmDrGdfyznDir2Rdo8aysOu42hvdpGOhQRERERERERkX2KxB5eVRrV\ni9Bh1x5eIiIiIs1ha3EZG7bson+3dFyJcZEOR0RERERERERknyKywsuyrPXA4fs6tjcOh/bwEhER\nEWkO862tAPzOqJ2hiIiIiIiIiESHSK7wapTqFV7+QIQjEREREWlZ5q0owG6zMayPCl4iIiIiIiIi\nEh2iv+ClFV4iIiIiTaZwh4ecTTvp27UNKUlqZygiIiIiIiIi0SF6C14OFbxEREREmtr8lQUA/M5k\nRTgSEREREREREZGGi96Cl117eImIiIg0tXnWVmygdoYiIiIiIiIiElWiuOClPbxEREREmlKpx8ea\njTvo1ak1rZPjIx2OiIiIiIiIiEiDRX/BSyu8RERERJpEfqGbINC9Q6tIhyIiIiIiIiIisl+ckQ7g\nt9IeXiIiItJSGGNuAk4G4oCngG+Al4AAsMSyrMnhiCN/mxuA7LbJ4fh2IiIiIgesYDDI6k+ew715\nPXZnHL3HX0ZSevs65/grylnyyt30PmUyrrbZ9c4p276Zle8/ATYbye260OsPF4c1j/vuuZOVlkV8\nfDx33n0fnTp3rnNOWVkZky6+kLvuvZ9u3brj8/m44/ZbyM/Lw+v1ctElkxg1ekz1+Q/+/W90796D\n086YENY8Cma+REXBBmzOOLKOu4i4NjV7zrrXLGD7Tx9hczhIHXAUrQeNIuj3seXz5/DuKMARn0Tm\nMecT16Ydm2c8ib90BwTBu7OAxA69aT/ur2HL5f4zB9OvY2vKvX5ueO1XNhSWVo8dO7AdV55o8PoD\nvP3TBt78YQNxDhv/OHcYXTKSKfF4ue2tRazfVkrXti4ePvcgAsEgVn4Jt729KGw5tKSfRzAYZN7b\nT1Gcl4PdGc+Is6aQ0rbue91X4WHWk1MZcfaVtMrqWO+cHZs2MPfNJwBIycpmxMQp2OzhW29yz2kD\n6ZfdinKfn5veWkRuYVn12DEDsrjiuN54AwHenbORt+bkEuewMW3iELpkuCgp8zL1vaV1Xo+3ju/H\n2q27eOPH3LDlAHDHKf3o2yGVcl+A295bysbtNXmM7pfJZWN64vMHeH9+Hu/OzSPOYeP+0wbSOd1F\nicfH3R8tI3d7GX07pHLryX3xB4JU+ALc+PZiitzesOQQDAaZ8cKjbF6/Bmd8PCdfch3p7bLrnFNR\n7uFf99/A+Euvp21253rnvPv4PezaUQRBKC7YTKfe/Tltym1hyaMql0XvPc2O/HU4nHEMnXAFyRm7\nv0fK+fHZqQybMIWUyvfInubsyFvL4g+ew2Z3YHfGcdBZV5OQ0rpJ443iFV6Ve3ippaGIiIhEMWPM\n0cBhlmUdDowCugAPA7dYlnU0YDfGjA9HLHkFoYJXRxW8REREJMYVLv+ZoM/H0Ivvp9uxZ5Pz2Ut1\nxkvy17DoxdvxFG3Z55y1n71Et2PPYshf7iEYDFC4/Oew5fH1zK+oqKjgldfeZMrV1/LQtL/VGV+2\ndAl/Of8c8jbW3NCe8e+PadMmjX++8hpPPfM8D9x3DwBFRduZPOlivvnvrLDFX8W9ej5Bv49OE+8g\nY+QZbJv9WvVYMOBn2+zX6XjaTXQ8/RZ2LpqFv3QnOxbPwh6fROeJd9B2zLkUzHwZgPbjJtPx9Fto\nf/KVOBKTaTv67LDlMXZIB+Kddv74j2954OPlTD11YPWYw25j6qkDmfh/P3DGo99z9hHdSE+J56wj\nurHL4+OUf3zL1HcWc++EwQBMPXUgf/94Gac/+j12Oxw/uH1937bJtZSfB8DGRT/i93k57pqHGHLy\n+Sx4f3qd8e0bVjPzsZtxb9u8zzmLPnmFIeP/zLFXT4Mg5C0J33v9+EHtiHfaOe3xH3jwE4vbxvev\nHnPYbdw6vj/nPD2HiU/8xMTDupCeHM+Zh3bBXe7j1Md+4K4PlnH3qQMASEuO48WLD+GYAe3CFn+V\nYwdkEe+0M/Hpn3n4s1XcNM7UyePGcYYLps/lvOfmcsbwzqQlx3H6IZ1wl/s58+k53Pfv5UytzP2W\nk/pyz4fL+fPz8/hq6VYuGdUjbHmsmPsdPp+Xi+55gmPPvJjP//V0nfH8tSt56a6rKdq6aZ9zTpty\nO3++/WEmXHs3ickpjD0/LM/DVtu0+CcCPi9HTZlGv3HnseSjF+qMF+eu5vsnb8ZduGWfcxZ/OJ3B\nf5rEEX+9jw6DDmXVzHebPN4oLnhphZeIiIi0CCcAS4wxHwIfA58AB1mW9W3l+KfAseEIJL8wVPDq\nkKGCl4iIiBzYjDEJzXn9nRuWk9Z7KACtOvehJH9NnfGgz0f/s24kqW3HvcxZC8Cu/DW07ha6AZve\n+yCK1oZvJc4vC+ZzxMgjARg8eAhLly6pM+71ennk/56iW/eaG8HHjz2RyVdcCUAgGMDpDDWIKi0t\n5bLJVzDupJPDFH0NT/5KXN1ChZ7EDr3wbMmpHqsozCeuTTvsCUnYHE6SOhnKNq7AW5hXPSc+rQMV\n2/PrXHP7j+/TeujxOF1Nu7pgbw7pmc5/l20F4Nd1RQzu0qZ6rHf7FHIK3Ozy+PAFgvy8ppBDe2fQ\nu31q9ZycrW56tksFYFDnNvy8ZjsAs5ZuZaTJDFseLeXnAVCwZhnZ/Q4GoG03w/bcVXXGA34vR158\nK6ntOu1zzsiLbiWzR3/8Pi+enUXEJbnClAUc0j2db1YUAPDrhmIGda75e+zVLoV1BW52lYdeW3PX\nbmdEr3R6t09h9vLQnJwCN73apQDginfy6Gcr+WDexrDFX+Xgbml8a20DYFHuDgZ2qsmjZ2Yy67eV\n4i734wsEmb+uiOHd0+nZLoVvVobyWLetlB5Zof+evfr1hazcsgsI1RI8Xn/Y8thgLaHXkOEAdOrd\nj/y1Vp1xv8/LmdfdTdvszg2e8993XmLE2D+S0jqtmaOva3vOMrL6hl7v6V0Nxbmr64wH/D6GX3Ar\nqVkd65+zMfRv6O/Ou55W2d0ACAYCOOKa/p/y6C14qaWhiIiItAxtgYOB04DLgNeo+xmtBAjLf/Xl\nb3OTlpqAKzFqu16LiIhIC2OMOckYs94Ys9oYU7uH3qfN+X395WU4E2puVtvsDoKBmi5DrboYElpl\nQDC4lzl2ggE/1Lp15YhPxO+paRnW3Ny7dpGSklr9tdPhJFArjyFDh9GuXTuCtfJISkrC5XLhdu/i\nuquv5PIrrwagY8dODBw0OGyx1xYoL8OekFT9tc3uIBgM5RGoKMNe++89LoFARRnxWd0oXfsrAJ78\n1fjcxdV5+kt3UrZhGakDjgxjFpCS6KSkrKalmi8QxGarGourM+Yu95GaGMeSjcUcMzC00mZYtzTa\nt0nEZqN6HsAuj4/UpLiw5AAt5+cB4POU1ilM2Xd7r7ft3g9Xm7bUfiPXN8dms+HevpVP759MuXsn\naR3Dt6Jo99eWv85ry0mJp9Zrq8JHSoKTpRt3MqZ/qBXl0K5tyGqdCEBeURmLcndgq/0iC5PkBCe7\nPL7qr2vnkZxYd6y03EdyopPl+TsZ1TeUx5DOrclqFSqiFO6qAGBYlzacdVgXXv5ufZiygPIyN4mu\nmgdJ7XZHnd+9nfsMoFV6Zp1/H/Y2x72zmJylvzD06LHNH/xufJ4y4hLr//cwvVtfktpk1Mnlf+bY\n7AQDARJTQ8W67TnLyfluBj2PbvoHKKL2boZdK7xERESkZSgElluW5QNWGmM8QKda46lAcUMulJmZ\nuu+T6rGrzEtRSTkHmaxGXSdSojHmphTL+Sv32BXL+cdy7qD8Y9CtwFBCDwS9Y4xJtCzrZaBZ78Q6\nEpLwVXhqDgSD+9yLZ89zHHUqE/4KD87E8K2mT05JodTtrv46EAhgb8CeQps3beKaKy/nzLPOYeyJ\nv2/OEBvEnpBEcPe/W1soD3t8EoGKmj1+AhUe7AkuknsezLbCPDa+dS+J2b1JyOpWfQN/16qfSel3\nWNhv6O/yhG7QV7HbamqmuzxeUmqNpSQ42VHq5YtFm+jTPpV3rz6CeWuLWLyhmGAQat8STUl0srMs\nPHsTQcv5eQA4E114PTXxBhvwXt/bnOT0LP4w9TnW/PgFC957nkPPvbp5At/N7q8tm81W67XlIyWx\npiCanOBkZ5mPL5dsplf7FN66/FDm5RSxJHdHWGLdG3e5j+SE2nnUvEfcu+WYnBAq8s1ctpVeWSn8\n69JD+GVdMUvzdlafc+Lg9lwyqjuX/nM+xaXhe48kJCVTXlbzcEMwGNzn7969zVn202wGHXFMhN4j\nSfjKa17vBAMNeI/UPyfvl29ZOfNdDr34DuKTWzV9vE1+xTBxVu/hpYKXiIiIRLXvgCnAI8aYbCAZ\nmGmMOdqyrNnAicDXDblQQUHJbw5idV7oP27atkpo1HUiITMzNepibkqxnL9yj83cIbbzj+XcIbbz\nj+FCX4VlWUUAlfuafm2M2UCdZ8mbXqsufdluzSdzwGHszF2JK6vLb56Tkt2DHeuW0rrbALavWkCb\n7oOaM/Q6hg47iG9mz+K4E8ayaOGv9O7TZ59zCrdt47JL/sLNt01l+IhDwxDlviVm98a99ldS+gzH\nk7+a+LY1z4fFZ2TjLd6C3+PGHpeAJ88i7XfjKN+8FleXAWSOOhvPlhx8Owur55SuX0r6oaeEPY+5\na7Zz7KB2/OeXTQzrlsaK/Job86s276JbZgqtkpyUVfgZ3iuDZ75azZCuaXxnbePu95cyqHNrOqaF\nVlYtyS1mRK8M5qwuZPSALL6vbAMXDi3l5wGQ2aM/eUt/psuwkWzLWUGbDl1/85xvnruHYX/8C6mZ\n2cQlJO2zKNCU5uUUMWZAFp8u3MzQrm2wNtX8W7l6yy66tXWRmujE4/VzSI90npu1lsFd2vDDqkLu\n+2g5Azu1qn5tRdKCdUWM6pfJ50u2MKRza1Zu3lU9tqbATdeMmjwO7pbGC9/kMKhTa35cXcgDMywG\ndGxFdmUeJw3twBkjOnHec3MpqbUyLBw6mwGsXPATAw49mtxVy2jXpXuj5qxdsoCj/nRuc4Zcr/Tu\n/diybC7ZQ45g+7oVtOrQ7TfPyZ03i/U/fc7IyfcRl5TSLPFGbcGrZg+vwD7OFBERETlwWZY1wxhz\npDHmZ0JPKl8GrAOmG2PigOVA0+/kupv8baEnf7Pbav8uEREROaCsM8Y8DNxuWVaJMeZPwOdAm33M\na5SMfiMoWrOQhc/fAkDvP17O1kXfEvCW0/7gWtur1nrafk9zAHqccB6rPnqGoN9HUmYn2g44rDlD\nr+OYY4/jpx+/5/yzzwTgrvv+xqczPqGsrIw/nXZ6rTRq8njh+WcpKdnJc888xbNPP4nNZuOpZ6cT\nHx8ftrh3l9zrd5SuX8LGN+8GIOuEiylZ8SMBbzmtB42i7dFnk//eNCBIq4FH40xpg83hoHDGuxTN\n+Qh7YjJZx19UfT1v0WbiWmeFPY/PFm7iqH6ZvH/NSACuffUXxh/ckaQEB2/+sIG731vCa5cfjs0G\nb/6wga07y6nwB7juD7/jirF92FFawfWvhdoC3vP+UqadNRSnw8bqzbuY8Uv+3r51k2opPw+ATkMO\nY7P1C18+fD0AI865inXzZuOv8NDz8BNqnWnb6xyA/sedzk+vPorD6cQRn8DwiVPClsfnizcz0rTl\nnSmh3y83vLGIk4Zl44p38NacXO79aDn/mjQCbPD2nFwKdpbj9QW49sQ+TD62FzvKvNz4Zt39BWu3\nOg2XL5du5fDeGbw+KbSX1S3vLmHckPYkxTt4d24eD3yyghf+8jtswLvzNlJQUoHXH2TK8YOZNKYH\nO8p83PbuEmw2uPWkvuQVe3ji3GEECTJ3bRFPzlyz9wCaSL9DjmTtovm8MPUKAMZPuoHF38+kotzD\nwWPG1Zxo2/ucKoWbNpKW1SEsse+uw6DDKFj5K98+Hopn2JlXsnHBbPwV5XQ99PiaE217mTPxKoKB\nAIs/nI4rLZOfX/wb2CCj50D6njCxSeO1ReKF2wSCi1Zs5tbn53D00GzOH9s30vGEVaw/0RaruUNs\n56/cYzN3iO38Yzl3gMzM1PCv1Y9uwca8Xt74ahVfzsvl1nMPpmfH8G4U3Vh6r8Ru/so9NnOH2M4/\nlnOH2M4/Vj8bGWOcwDnA25ZllVYeawfcbFnWVQ25xkVvLYnKm1+1TZ8wEIAwL1JoclXdyMY9+3Nk\nA2kCMy4dTufJH0U6jEbLfXJ8i/l5ANz5xaoIR9I4dx7fG4DuV8+IcCSNl/PIOPre9Hmkw2i0FQ+c\nwBu/5EU6jEabOKwjN8ywIh1Go00bZ2AvbY1bwAqvqP/MIiIiIhJx+YWhFV4dMrTCS0RERA4clfuc\nvrTbsS1Ag4pdIiIiEjvC10i0iTm0h5eIiIhIk8nf5iYtNQFXYtQ+DyUiIiIiIiIiMSxqC1527eEl\nIiIi0iRKPV6KSsrpqP27RERERERERCRKRW3By+FQS0MRERGRppBfWApAtgpeIiIiIiIiIhKlorbg\n5axa4aWWhiIiIiKNkr8ttH+XCl4iIiIiIiIiEq2ituBVvYeXVniJiIiINEpeQajgpZaGIiIiIiIi\nIhKtorfgVdnSMKA9vEREREQaJb8wVPDqkKGCl4iIiIiIiIhEp+gteNm1h5eIiIhIU8jf5iYtNQFX\nojPSoYiIiIiIiIiI/CZRW/Cy2WzYbTZ8KniJiIiI/GalHi9FJeVqZygiIiIiIiIiUS1qC14AdrsN\nv18FLxEREZHfKr+wFIBsFbxEREREREREJIpFdcHL4bDh1x5eIiIiIr9Z/rbQ/l0qeImIiIiIiIhI\nNIvqgpfTbtMeXiIiIiKNkFcQKnippaGIiIiIiIiIRLOoLng57DYCKniJiIiI/Gb5haGCV4cMFbxE\nREREREREJHpFd8HLYdceXiIiIiKNkL/NTVpqAq5EZ6RDERERERERERH5zaK74GXXHl4iIiIiv1Wp\nx0tRSbnaGYqIiIiIiIhI1IvqgpfdbsOnloYiIiIiv0l+YSkA2Sp4iYiIiIiIiEiUi+qCl8NuU0tD\nERERkd8of1to/y4VvEREREREREQk2kV5wcuOXyu8RERERH6TvAIVvERERERERESkZYjugpdDe3iJ\niIiI/Fb5hZUFrwwVvEREREREREQkukV1wctptxHQCi8RERGR3yR/m5u01ARcic5IhyIiIiIiIiIi\n0ihRfXdDe3iJiIiI/DalHi9FJeUM6J4e6VBEREREmtX0CQMjHUKTaSnPKc24dHikQ2gSuU+Oj3QI\nTaKl/DwA7jy+d6RDaBI5j4yLdAhNYsUDJ0Q6hCYxcVjHSIfQJKaNM5EOodlF9Qovu91GELTKS0RE\nRGQ/5ReWAtBR+3eJiIiIiIiISAsQ1c+FOByhep0/EMBud0Q4GhEREZHokb+tcv8uFbxERESkhXvw\nv2sjHUKjXT+qBwDdr54R4Ugap2rVyhu/5EU4ksabOKwjd36xKtJhNNqdx/fmhhlWpMNotKqVK0nH\n/T3CkTRO2Zc3AjDyoW8jHEnjfXfdkZzz6sJIh9For54zhIUbSiIdRqMN6ZLaot7r9YnqFV4Ouw0A\nn9oaioiIiOyXvAIVvERERERERESk5WgRBS+/WhqKiIiI7Jf8wsqCV4YKXiIiIiIiIiIS/aK74FXZ\n0lB7eImIiIjsn/xtbtJSE3C1lJ3PRURERERERCSmRXXBy6kVXiIiIiL7rdTjpaikXO0MRURERERE\nRKTFiOqCV3VLQ38gwpGIiIiIRI/8wlIAOqrgJSIiIiIiIiItRFQXvOxa4SUiIiKy3zZXFrw6ZLgi\nHImIiIiIiIiISNOI6oJX1R5ePhW8RERERBpsV5kXgFau+AhHIiIiIiIiIiLSNKK74KWWhiIiIiL7\nrbQ8VPByJTojHImIiIiIiIiISNNoEQWvQFArvEREREQayu3xAeBKjItwJCIiIiIiIiIiTSO6C16O\nqhVeKniJiIiINFRpZcErWSu8RERERERERKSFiO6Clz0Uvl97eImIiIg0WGn1Ci8VvERERERERESk\nZYjIXQ5jzAjgAcuyRhtjhgKPAz6gHDjPsqyChlxHe3iJiIiI7L9SjxeH3UZCnCPSoYiIiIiIiIiI\nNImwr/AyxlwPPA8kVB56FJhsWdYY4APgpoZeq7rgpRVeIiIiIg3m9vhwJTqx2WyRDkVERERERERE\npElEoqXhauCPtb6eYFnW4so/O4Gyhl6oag8vnwpeIiIiIg1W6vHiSlA7QxERERERERFpOcJe8LIs\n6wNC7Qurvt4CYIw5HJgMPNLQa1Xt4RVQwUtERESkQYLBYOUKr7hIhyIiIiIiIiIi0mQOiEd7jTET\ngJuB31uWVdiQOZmZqbRplQhAckoCmZmpzRjhgSfW8q0tlnOH2M5fuceuWM4/lnOX5lHhC+APBElO\nPCA+BoqIiIiIiIiINImI3+kwxpwDXAKMsiyruKHzCgpKKC2rAKCoqJSCgpJmivDAk5mZGlP51hbL\nuUNs56/cYzN3iO38Yzl3ULGvuZR6QgvtXSp4iYiIiIiIiEgLEok9vKoZY+zAY7QpR4IAACAASURB\nVEAK8IEx5mtjzB0Nne+o3Gjdr5aGIiIiIg3i9ngBSFZLQxERERERERFpQSLyaK9lWeuBwyu/zPit\n13E4VPASERER2R9a4SUiIiIiIiIiLVFU3+lw2EML1Pz+QIQjEREREYkOVSu8VPASERERqV8wGOT7\n159g+8YcHHHxHHnulbTK7FDnHF+Fh08fvZWjzr+a1u067XPO6p9nsWzWvzn5xofDmss9pw2kX3Yr\nyn1+bnprEbmFZdVjxwzI4orjeuMNBHh3zkbempNLnMPGtIlD6JLhoqTMy9T3lrKhsLR6zq3j+7F2\n6y7e+DE3bDkEg0FmvPAom9evwRkfz8mXXEd6u+w651SUe/jX/Tcw/tLraZvdud457z5+D7t2FEEQ\nigs206l3f06bclvY8pj39lMU5+Vgd8Yz4qwppLRtX+ccX4WHWU9OZcTZV9Iqq2O9c3Zs2sDcN58A\nICUrmxETp2Czh6eZVzAYZNF7T7Mjfx0OZxxDJ1xBcsbueZTz47NTGTZhCimVeexpzo68tSz+4Dls\ndgd2ZxwHnXU1CSmtw5JHlcemHM/gHll4Knxc9vCnrNu8o3rs94f25OazD8frD/DK54t56dNFOOw2\npt8wjq7tW+PzB/jrw5+xOq+oes6E0f2YNP5gRl/1aljzuPbYXvTKTKbCH+Dvn68if4eneuyIHumc\nf1gXfIEA/1myhU8Wb8Fpt3HL2D5kt0nEXe7jH1+tIX+Hh16ZyUz70wByi0K/Kz78dROzVm4LSw7B\nYJD1n06ndMt67M44uv1hEolp7arHi1fOI//b97DZnbQdOorMYccQ8PvI+fgpyou24Eh00XXsX0hM\nb497Uw7r//M8dmccSe270fWEC8KSQ1Ue0x9/gPVrVxEXH8+ka26jXYdOdc4p93i496bJXHbdVLI7\nda0+vmr5El5/4f+446FnAdi4fi3PPXo/AO07dmbSNbdjD9N7vSqXaHq/R7SlYWM57KEVXj6t8BIR\nERFpkKoVXmppKCIiIlK/9b/+QMDn5eQbH+aQP/6ZOe88X2d82/pVfPLQDZRs29ygOds2rGbl91+E\nLf4qxw9qR7zTzmmP/8CDn1jcNr5/9ZjDbuPW8f055+k5THziJyYe1oX05HjOPLQL7nIfpz72A3d9\nsIy7Tx0AQFpyHC9efAjHDGhX37drNivmfofP5+Wie57g2DMv5vN/PV1nPH/tSl6662qKtm7a55zT\nptzOn29/mAnX3k1icgpjz58ctjw2LvoRv8/Lcdc8xJCTz2fB+9PrjG/fsJqZj92Mu9brqr45iz55\nhSHj/8yxV0+DIOQt+TlseWxa/BMBn5ejpkyj37jzWPLRC3XGi3NX8/2TN+Mu3LLPOYs/nM7gP03i\niL/eR4dBh7Jq5rthywPg5CN6kxDnYPRVrzL1xdlMmzSmesxht/H3SWP4/Y1vcfy1b/CX3w+hbesk\nxo7oicNuZ8xVr/G3V3/g7guPrp4zpGcW540dHNYcAI7qlUG8w8Zlbyzk2W/Wcfmo7jV52ODy0T24\n6p3FXPHWYk4e3IE2SXGcPLg9pRV+Jr2+kEe/Xsu1x/YEwLRL4c15G7ny7cVc+fbisBW7AIqtuQR9\nXvpfcC+dxpxF7hcvV48FA342fPkK5pzbMefdQcGCr/C6d1Kw4Csc8Yn0v/A+up5wAes/C7221s14\nji5jL6Dv+XfhTHBRuPi7sOUx9/v/4vVWcO9jL3LWhZfz8jOP1Blfu3I5d157CVs35dU5/vHbr/Ds\nI/fi9Xqrj73xz6c46y+Xc/cj0yEI83/6Jiw5VIm293t0F7wqWxoGVPASERERaZDqloYJWuElIiIi\nUp/Nq5fRacDvAMjq3peC9SvrjPt9Po67bCqt23fe5xzPrp3M/+gVDpswKUzR1zikezrfrCgA4NcN\nxQzqXPMkfa92KawrcLOr3IcvEGTu2u2M6JVO7/YpzF4empNT4KZXuxQAXPFOHv1sJR/M2xj2PDZY\nS+g1ZDgAnXr3I3+tVWfc7/Ny5nV30za7c4Pn/Pedlxgx9o+ktE5r5uhrFKxZRna/gwFo282wPXdV\nnfGA38uRF99KartO+5wz8qJbyezRH7/Pi2dnEXFJrjBlAdtzlpHVNxRTeldDce7qOuMBv4/hF9xK\nalbH+udsXAPA7867nlbZ3QAIBgI44hLCkEGNwwd24su5OQDMXbGJg/rUrMrs2yWD1XlFlJRW4PMH\n+GFJHiMHdWbVxu04HaHb6q2TE6jw+QFIT03kjguO4rqnZoY1B4DBnVoxZ11oldmyzSX0bZ9aPdY1\nw8XGojJKK/z4A0EW5e1gaOfWdMtw8VPOdgByi8rokh56DfVtn8LhPdL5vwmDufH43iTGha+EUJK7\ngtY9hwKQ0rE37k1rq8fKCvJITO+AI8GF3eEkpUs/StYvw7NtI617DQMgMSMbz7Z8ALwlhaR07B26\nVidDSe6KsOWxYsmvDD0ktKNT734DWbtyeZ1xn8/L9Xc9RHaXbnWOt8/uzHV3PlTn2HV3PEjfgUPx\neb0UFxXiSk5p1th3F23v9+gueFW1NFTBS0RERKRBqloaJquloYiIiEQhY0ySMabZ74h7PaXEJyVX\nf213OAgGarbUaNezH8lpbSEY3Oscv8/Lt/96lBGnX4IzPrHO+eGQkuikpKxmpYA/EMRmqzXmqRlz\nV/hISXCydONOxvTPAmBo1zZktU4EIK+ojEW5O7BVXSCMysvcJLpq/d3aHQRq/Tw69xlAq/RMCDZs\njntnMTlLf2Ho0WObP/hafJ7SOoUpu73u66pt93642rSldiL1zbHZbLi3b+XT+ydT7t5JWsceYckh\nFFMZcYk1Mdl2yyO9W1+S2mTU+Xn8zxybnWAgQGJqqOC4PWc5Od/NoOfRJzd/ArWkuhLYUVpeE6c/\nUP0eaZWcwE53zVhJWQWtkhNwl3np2r41C1+8iP+76gSe+nA+Nhs8fc2J3PjM17g9FYT7beKKd7Cr\n3Ff9tT8QpCqE5Hgn7lpjpRV+kuMdrNy6i8N7pgMwoEMqmSnxACzdVMKTs3O44q1F5O/wcOHhNe32\nmpu/vBTH7q+tYOi15a8oxZGQVD3miEvEX1GKq313ilfNB2DXxpV4S7YTDAZJSGtHyYZQoal41TwC\nXg/hUlrqxuWqKUw5HHV/Z/XpP5j0tln/82/C8JGjcTgcdY7ZbDa2bd3MtRdPoGRnMV179Gne4HcT\nbe/36C54Va7w0h5eIiIiIg1TvcJLLQ1FREQkChhj+htjPjTG/NMYcyywHFhmjPlDc37fuEQXXk/N\nvlXBQHCf+yPtac72jTns3LqJ719/glnTH6B4cy4/vf1cs8W9u10eX50HnWw2W/X91V0eHym1PhMm\nJzjZWebjnZ9z2VXu463LD+W4ge1Ykrtj98uGXUJSMuVltf5ug8F97mGztznLfprNoCOOCXvxzpno\nwuup2UMtGNz362pvc5LTs/jD1OfoNfJEFrz3fH2XaHLOxCR85TUxEQw0II/65+T98i0L33uGQy++\ng/jkVs0Rcr1KSstJTYqv/tpur3mP7HSXk+qqqa+nJsWzw13OFaf+ji/nrWXIhdMZcek/mX7DOA7r\n35EeHdN4fMrxvHLLyZguGfz90jG7f7tmU1rhxxVf816322rqD+4KX50OH654ByXlPv6zZAulFX6e\nmDCYkb0ysLbsAuDbVYWs2uoG4JtV2+idWVM4bm6OBBf+8lqFqWAAmy30OnHEu/DXeg35K8pwJCTT\ndshoHPFJLH/5Doqsebg69MBms9H9pMvY9N0HWK/egzO5DU5X+F5bLlcynjJ39deBQKBR+261zWrP\nYy+9z3Hj/sTLT4d3H8hoe79Hd8Gr8h8lrfASERERaRh3dcFLK7xEREQkKjwDPAL8F3gXGA4MA25u\nzm/armd/cpfMBWDr2uWkd+z2m+ZkduvDqXc8zbhrHmD0xTfRpkMXDj3jkuYMvY55OUWM6lezWsva\nVFI9tnrLLrq1dZGa6CTOYeOQHuksWF/E4C5t+GFVIROe+IlPF25iQ2FpfZcPm85mAKt+nQNA7qpl\ntOvSfR8z9j5n7ZIF9Bo6vHmC3YvMHv3JXzYPgG05K2jTYd8rZ+qb881z91BSEGrdFpeQtM8b0E0p\nvXs/tiwPxbR93Qpadej2m+fkzptFzvczGDn5PlzpWc0Vcr1+XJrHCcNDq+OG98tmSU5B9diKDYX0\nzG5D6+QE4px2jhjUiTnL8igq8VSv/Cp2l+N02FmwaguHXPIiJ97wJufd9zEr1hdy47Nfhy2PRXk7\nOax7aPXMgA6prNlW875dX1hKpzaJpCQ4cNptDOnUmqX5O+nXPpX5G4q5/K1F/HflNvJ3hApND582\nEFPZyvTgrm2qC2HhkNLJsGP1AiC0Wispq0v1WFJmR8q3b8bncRPw+yjZsIKUTn1w56+mVfdB9Dv/\nLtL7H0pCm9DrqHjVAnr8cQrmnNvxle6kdffw7a1mBgxhwc/fA7By2WK6dO+1X/ODtVZ+TZt6DZvz\ncgFIdCVjd4S3pBNt7/eovtNRvcJLBS8REREJM2PMd8B04G3LsiJ/F6CByipbWailoYiIiEQJu2VZ\ns4HZxpjRlmVtBTDG+PYxr1G6DTucvOUL+HjatQAcff7VrPn5v3grPPQdWasNXq0VQnuaE2mfL97M\nSNOWd6YcBsANbyzipGHZuOIdvDUnl3s/Ws6/Jo0AG7w9J5eCneV4fQGuPbEPk4/txY4yLze+uajO\nNYNhbssI0O+QI1m7aD4vTL0CgPGTbmDx9zOpKPdw8JhxNSfa9j6nSuGmjaRl1ezVFC6dhhzGZusX\nvnz4egBGnHMV6+bNxl/hoefhJ9Q607bXOQD9jzudn159FIfTiSM+geETp4Qtjw6DDqNg5a98+3jo\n73TYmVeyccFs/BXldD30+D2l8b9zJl5FMBBg8YfTcaVl8vOLfwMbZPQcSN8TJoYtl4++W8mYg7rx\n9aNnA3DJg//hjNH9cCXG8dKni7jxma/55IEzsNlsvPTZIjZvd/N/78/j2WtP5Mt/nEWc087UF2bj\nqWjWX0n79M2qQg7pmsZTE0NFnb99topj+2aSGGfnk8Vb+L//ruXh0wZhs8EnizdT6Pbi9Qe564i+\nnDeiCyXlPh74LLTv4INfruaaY3riDQTZ7q5g2her9vatm1Ra3+HszFnE8n/eDkD3ky+jcMl3BLzl\nZA47hs7Hn8/K1+4lGITMYaOJT03D7nCy5v1Hyf/ufZyJyXQ/6TIAEtM7YP3rbuzxCaR2HUDrXkPD\nlsfwkaNZtGAOt195IQCXXX8H3339GeUeD8f8/pSaE+tZZVp79ekpZ17Akw/eSVxcPPGJiUy65rZm\njX130fZ+t0XiH6kmECwoKGFt/k7ufWUeY4d34Ywx+1cljWaZmakUFJTs+8QWKJZzh9jOX7nHZu4Q\n2/nHcu4AmZmp4d8cYD8YYz4HjgHcwFvAC5ZlzYlgSMGGvF7+9up8Vm/cwfM3jsYegf0XmoPeK7Gb\nv3KPzdwhtvOP5dwhtvM/0D8bNRdjzAuEOnNdYllWoPLYTcAwy7ImNOQaD/53bVTe/Krt+lGhFSjd\nr54R4UgaJ+eRUHHqjV/yIhxJ400c1pE7w1gIaC53Ht+bG2ZYkQ6j0aaNMwAkHff3CEfSOGVf3gjA\nyIe+jXAkjffddUdyzqsLIx1Go716zhAWboj+zx5DuqS2pPd6vZ+JoruloV0rvERERCQyLMs6AegG\nPASMAX4wxiw1xlxtjGkb0eD2otTjw5XobDHFLhEREWnxLgb+XVXsqrQRuCBC8YiIiMgBKqp72dS0\nNAzs40wRERGRpmdZ1kbgHuAeY8zRwJnA9cDfjDH/Bp6xLGtmJGPcndvj1f5dIiIiEjUqC10f7Xbs\n1QiFIyIiIgcwrfASERERaRpbK/9XDMQT2kz9S2PMHGNM74hGVkupx4crIS7SYYiIiIiIiIiINKmW\nUfDyq+AlIiIi4WeMSTfGTDbG/AwsAf4KfAYMsCyrFzAQSAPeiGCY1by+ABW+gFZ4iYiIiIiIiEiL\nE9V3Oxz2UL1OLQ1FREQk3Iwx7wHjgDjga+As4APLsiqqzrEsa5kx5nXgmshEWVdpuQ+AZBW8RERE\nRERERKSFieq7HTV7eGmFl4iIiITdocBDwAuWZeXs5bxZwK97u5AxZj6wo/LLHOB+4CUgACyxLGty\no6MFSj1eAFyJamkoIiIiIiIiIi1LdBe8tIeXiIiIRE5ny7ICxpj2VQeMMWmVxxdVHbMsa/beLmKM\nSag8b0ytYx8Bt1iW9a0x5mljzHjLsj6q9yIN5PZohZeIiIiIiIiItExRfbejuqWh9vASERGR8Esx\nxrwDdAX6Vh4bAfzHGPMhcLZlWWUNuM4QINkY8zngAG4FDrIs69vK8U+B44BGF7xqVnhF9UdAERER\nEREREZH/YY90AI2hloYiIiISQX8DDgLuqnVsNnAmcARwRwOvUwo8aFnWCcBlwGuArdZ4CdC60dEC\npZUrvNTSUERERERERERamqh+vLempWEgwpGIiIhIDBoPXGdZ1htVBypXdL1tjEkFbgduasB1VgKr\nK+evMsYUEiqkVUkFihsSUGZm6l7H7c4CADpkpu7z3GjT0vLZX7Gcv3KPXbGcfyznDspfRERERPas\nZRS81NJQREREwi8N2FzP2AagXQOvcyEwCJhsjMkGWgFfGGOOrtz/60Tg64ZcqKCgZK/jW7btAsDn\n9e7z3GiSmZnaovLZX7Gcv3KPzdwhtvOP5dwhtvNXoU9ERERk76K64GWz2bDbbGppKCIiIpGwlFD7\nws/3MHYasKKB13kB+Kcx5lsgAPwZKASmG2PigOXAu42OFnBXtjRMVktDEREREREREWlhorrgBaF9\nvFTwEhERkQh4BHjNGJMBvA9sATKBUwi1Ozy3IRexLMsLnLOHoVFNE2aN6j28EqL+I6CIiIiIiIiI\nSB1Rf7fDYbdpDy8REREJO8uy3jDGtAbuAv5Qa2g7cIVlWa9HJrL6lZZXFrwSo/4joIiIiIiIiIhI\nHfZIB9BYoYKXVniJiIhI+FmW9QzQHhhIaEXWUKC9ZVlPRTKu+pR6vIAKXiIiIiIiIiLS8kT93Q6H\nw47fr4KXiIiIRIZlWUFg2e7HjTGDLctaFIGQ6uX2+EiMd+CwR/0zTyIiIiIiIiIidUR/wUstDUVE\nRCQCjDFtgGnAaCAesFUO2YFkoBXgiEx0e1bq8ZKs1V0iIiIiIiIi0gJF/eO9amkoIiIiEfIIcAGw\nEvACRcBcQg8UpQKXRi60PXN7fLgS4yIdhoiIiIiIiIhIk2sZBS+1NBQREZHwOxG4y7KsccCzQI5l\nWacChlCLw96RDG53/kAAT4UfV4JWeImIiIiIiIhIyxP9BS+HXSu8REREJBLSge8q/7wU+B2AZVk7\ngIeAUyIU1x6VlfsBcKmloYiIiIiIiIi0QNFf8FJLQxEREYmMIkJ7dQGsBjoYY1pXfr0O6BSJoOrj\n9ngBSFZLQxERERERERFpgaL+Ed9QwSsQ6TBEREQk9nwDXGWM+QZYA5QApwIvAiOB4gjG9j9KPT5A\nK7xEREQk9lw/qkekQ2gyOY+Mi3QITWLisI6RDqFJ3Hn8AdXF/DebNs5EOoQmU/bljZEOoUl8d92R\nkQ6hSbx6zpBIh9AkhnRJjXQITaIlvdfrE/V3PLSHl4iIiETIPYSKXv+2LGuUMeZp4GljzJXAAODJ\niEa3m6qCV7IKXiIiIhJjRj70baRDaLSqm9+nvjg/wpE0znsXHgzAyi2lEY6k8fq0c3HOqwsjHUaj\nvXrOEF6auyHSYTTanw/pAkDfmz6PcCSNs+KBEwC484tVEY6k8e48vjdD7pgZ6TAabeFdx3DDDCvS\nYTTatHGGcc/+HOkwGm3GpcP3Oh71dzyqWhoGg0FsNlukwxEREZEYYVnWImNMP2Bw5aFbgF3AUcCH\nwP2Rim1PqloautTSUERERERERERaoOgveDlC25AFgkEcKniJiIhImBhjHgNesSzrcwDLsoLAfZX/\nO+CopaGIiIiIiIiItGT2SAfQWA57qMgVCKitoYiIiITVxUBGpINoqKoVXmppKCIiIiIiIiItUYsp\nePm0j5eIiIiE10JgaKSDaKiaFV5qaSgiIiIiIiIiLU/UP+Jb1dLQrxVeIiIiEl4zgLuNMWOBXwnt\n31Vb0LKsO8If1p6VlocKXlrhJSIiIiIiIiItUdTf8bBXrvBSwUtERETC7O7K/x9V+b/dBYEDpuDl\nrlrhlRD1H/9ERERERERERP5H1N/xcFYVvPyBCEciIiIiMSaqegOWVu7hpZaGIiIi0pyMMcfvz/mW\nZX3RXLGIiIhIbIn6gpdDK7xEREQkAizL8kc6hv3h9viId9qJc0b9Fq4iIiJyYPuM0Ep3217OqRoP\nAo5wBCUiIiItX/QXvByhz08BFbxEREQkjIwxr+zrHMuyzgtHLA1R6vHi0v5dIiIi0vxGRzoAERER\niU1Rf9fDYQ89pexTwUtERETCawyhp5JrSwVaAYXAL2GPaC9KPT7apCREOgwRERFp4SzLmh3pGERE\nRCQ2tYCCl/bwEhERkfCzLKvTno4bY4YA7wDPhjei+gWCQUrLfXRomxzpUERERCTGGGOSgcnACUA2\ncFrln+erOCYiIiJNKeo3cahqaag9vERERORAYFnWQuBO4K4Ih1LNU+4nGITkhKh/1klERESiiDGm\nHTAPuJfQKvg+QAKhtoefG2OOimB4IiIi0sJEfcHLblfBS0RERA44W4GekQ6iSqnHC4ArMS7CkYiI\niEiMmQYkAQY4DLBVHj8N+AmYGqG4REREpAWK+sd8q/bwUktDERERCSdjzJ4eHHIAnYGbgZzwRlQ/\nt8cHQHJi1H/0ExERkegyDrjasqwcY4yj6qBlWeXGmIeBf0YuNBEREWlpov6uh7NyhVdAK7xEREQk\nvHxAfR9AbMA5YYxlr0rLQwUvlwpeIiIiEl4uoKCesXIgMYyxiIiISAsX9Xc9tIeXiIiIRMj9/G/B\nKwjsBP5tWZYV/pD2rKqlYbJaGoqIiEh4/QJcAHy2h7E/AQvDG46IiIi0ZNFf8KpsaehTwUtERETC\nyLKs23Y/ZoxxAn7Lsg6oDyZVLQ21wktERETC7B5ghjFmJvAxoYeDjjfGTAbOB06NZHAiIiLSsuxp\n74lmZ4wZYYyZVfnnnsaYb40xs40xT+7vtRyVLQ39/gPqvpKIiIjEAGPMzcaY2k8sjwS2GGOmRCqm\nPSlVwUtEREQiwLKsz4AJQE/gEUJtn+8Hfg/8xbKsjyIYnoiIiLQwYS94GWOuB54HEioPPQzcYlnW\n0YDdGDN+f65nryp4BQJNGaaIiIjIXhljrgHuBVbVOrwWeBf4hzHmLxEJbA/camkoIiIiEWJZ1ruW\nZXUD+hJ6OGggkG1Z1ssRDUxERERanEg85rsa+CPwr8qvD7Ys69vKP38KHAc0+Akf7eElIiIiEXIp\nMNWyrPuqDliWtQH4qzFmM3AV8EKkgquttFwrvERERCRyjDGZQH8gDdgK5APFEQ2qAa49the9MpOp\n8Af4++eryN/hqR47okc65x/WBV8gwH+WbOGTxVtw2m3cMrYP2W0ScZf7+MdXa8jf4aFXZjLT/jSA\n3KIyAD78dROzVm4LSw7BYJBNX7xI2dYN2J1xdDzxYuLbtKse37l6PgU/fIDN7qTNoKNJHzKagN9H\n3n+eoaJ4K44EFx2Ou4CEtHaUbV1P/ucvYLM7SUhvT8cTLwlLDlV5PP3w/eSsXkl8fAJX3DiV9tmd\n6pzj8ZQx9dq/cuVNd9Kxc9fq49ayxbz87OPc/9jzAKxdZXH3TVPo2Cl0zomnnM7I0ceFLY/1n06n\ndMt67M44uv1hEolpNT+P4pXzyP/2PWx2J22HjiJz2DEE/D5yPn6K8qItOBJddB37FxLT2+PelMP6\n/zyP3RlHUvtudD3hgrDkUJXH5y89ztYNa3HGxXPiRdeQltWhzjnecg9v/v0mfn/xdWR06FTvnNKd\nxfznhUcod+8iEAhw0qQbaLPbtZrbHaf0o2+HVMp9AW57bykbt5dVj43ul8llY3ri8wd4f34e787N\nI85h4/7TBtI53UWJx8fdHy0jd3sZfTukcuvJffEHglT4Atz49mKK3N6w5BAMBpn39lMU5+Vgd8Yz\n4qwppLRtX+ccX4WHWU9OZcTZV9Iqq2O9c3Zs2sDcN58AICUrmxETp2Czh2/dzK1/MPRpl0KFL8Cd\nHy8nr6jmd+/RfdpyydHd8AaCfPTLJj5YkI/TYePuU/rTKS2JXR4f98+w2FhURo/MZG4/qS8AGwpL\nufPj5QTDVEIIBoMseu9pduSvw+GMY+iEK0jO2P3nUc6Pz05l2IQppFT+PPY0Z0feWhZ/8Bw2uwO7\nM46DzrqahJTW4UmkMpeCmS9RUbABmzOOrOMuIq5NVvW4e80Ctv/0ETaHg9QBR9F60CiCfh9bPn8O\n744CHPFJZB5zPnFt2rF5xpP4S3dAELw7C0js0Jv24/7apPGGfYWXZVkfAL5ah2y1/lwC7NdPSy0N\nRUREJEI6Az/UM/YdodY9B4SqloZa4SUiIiLhZIxxGGMeAzYC7xN6GOjfQJ4x5paIBrcPR/XKIN5h\n47I3FvLsN+u4fFT36jGHDS4f3YOr3lnMFW8t5uTBHWiTFMfJg9tTWuFn0usLefTrtVx7bOjjoGmX\nwpvzNnLl24u58u3FYSt2AZSsmkfA76PnuXfR7ugz2fT1q9VjwYCfzV+/SrcJt9B94m0ULZyJr3Qn\nRQu/xh6fRM9z76bDseez6ct/AlDw/ftkHXEqPc6eSsDnpWTNL2HL46dvZ+H1ennw6Zc579IrmP7E\nP+qMr7aWcfOUi9iSv7HO8fffeJknpt2Dt6KizrmnTDiX+x57jvseey5sxS6AYmsuQZ+X/hfcS6cx\nZ5H7Rc1Cx2DAz4YvX8GcczvmvDsoWPAVXvdOChZ8hSM+kf4X3kfXEy5g6Mbd6QAAIABJREFU/Weh\nZ+rWzXiOLmMvoO/5d+FMcFG4+Luw5bFy3vf4vV7Ou+Mxjj7jQma+9kyd8U05K3n13msp3rppn3O+\nfuN5Bh5xDGff9g+OOu3PFG7KDVseAMcOyCLeaWfi0z/z8GeruGmcqR5z2G3cOM5wwfS5nPfcXM4Y\n3pm05DhOP6QT7nI/Zz49h/v+vZyp4/sDcMtJfbnnw+X8+fl5fLV0K5eM6hG2PDYu+hG/z8tx1zzE\nkJPPZ8H70+uMb9+wmpmP3Yx72+Z9zln0ySsMGf9njr16GgQhb8nPYctjTN9M4h12zn9hPo9/tYbr\nTuhTPeaw27h2bG8ueeUXLvrnAk47OJs0VxynHtyR0nIf502fx98/tbil8md4xTE9eOzL1Vzw4nxs\ntlCxLFw2Lf6JgM/LUVOm0W/ceSz5qO6zsMW5q/n+yZtxF27Z55zFH05n8J8mccRf76PDoENZNfPd\nsOUB4F49n6DfR6eJd5Ax8gy2zX6teiwY8LNt9ut0PO0mOp5+CzsXzcJfupMdi2dhj0+i88Q7aDvm\nXApmhn7XtR83mY6n30L7k6/EkZhM29FnN3m8B8JjvrV7EabSwCd8MjNTAUhr4wLAlZxQfSwWxFKu\nu4vl3CG281fusSuW84/l3KNAHnAEMGsPY4cAm/dwPCKqWhq6Eg6Ej34iIiISQ24DJgNPAO8RWt3V\nHjgLuNsYU2xZ1lMRjK9egzu1Ys66IgCWbS6hb/uaz+VdM1xsLCqjtMIPwKK8HQzt3JpuGS5+ytkO\nQG5RGV3SQ/es+rZPoXNaEkf2asvGojIem7UGjzc8W3O4N1qkdh8CgCu7F2WbcqrHygv/n737Do+y\nSvs4/p2SntBDCx3hgDSxIPbe1t4rthW77rJrWwsKdl9lXcvay9pWXQu2tS2ioqLSpHMIvfcWUqe9\nfzyTppAEk3kemPw+15WLmTlzJvfJpAznnnPfy0lt3pZAWnxvrUMvCpfMpnTdcnK6OXPSWrSjdP0K\nANJbdyZSXEAsFiNaVgL+gCtrAJg1bQp7DtofALN7P+bZWdXGw6EQt9/7d0bdc3u129vldeTWex+p\ndvu8ubNZsXQxP44bS/sOnbj8+ptIz8hI/CKAgqVzaNp9DwCy83pQuHJBxVjx2uWkt2hX8Xxkd+pN\nweJZlKxbRtPdBgKQ3rI9Jeuc5yNUsJ7svB7OfTsYNs6dSMt+B7qyjmVzZ9Ct/z4A5O3Wm1UL5lYb\nj4TDnDFsBB89/eD25yx0KsMvy59J687d+fcDN9Msty1HDmnY0x612atLc8ZZJwk9belm+naoPIvR\nPTeLxeuKKCx1ftYnLdrIoK4t6N4mm2/nrgVg0boiurXOAmDYG1NZv9VJrgb8PkpCEdfWsXb+LNr3\n3guAVl0MG5bmVxuPRkIcNPQ2xr8yqtY5B152Gz6fj0g4RMmWjaRkZLq0ChjYuSnfz1sPwPTlW+jT\nvvJ3b9dWmSxZX/l8TF6yib26NKdbbhbf5TtzFq8vpmuuE++wN6cDEAz4aJmdytbSqmdwEmvDwlm0\n7uV8bVt0NmxaOq/aeDQSZtAltzH5jVHbn7NsPgB7X3gj6TnNAYhFowRS0nBTyYq5ZHbpD0B6u90o\nWV35d6Rs/QpSmrXBn+b8Ds3oYCheNofQ+uUVc1Kbt6Nsw4pqj7lh/Hs03eNogpkNf1JtZ9j1mGyM\nOdha+y1wHPBVXSatXVsAQFFhKQCbNxdV3JbscnNzGs1af60xrx0a9/q19sa5dmjc62/Ma4ddItn3\nKnCrMaYI5x3Lq4Fc4BTgDuCRGua6qqgkTMDvIzXF9cP9IiIi0rhdCtxvrb2jym0W+MYYsxX4C7BD\nCS9jTGtr7ZoGjHGbMlMD1TZHI9EYPiAGZKUGKawyVlQWISs1wNw1W9m/ewu+m7+BPu1yyM1OBWDm\nygI+nLaK/DWFDNm3I5fu35l/frMQN0RLiys2IgF8fj+xWBSfz0+ktLgiuQLgT0knWlZMepsuFMyb\nQpMee1O0PJ/Q1o3EYjFSm7dl5Zcvs3b8B/jTMsjqtLsrawAoKiokKzu74nogECAajeKPl1nr1ddJ\n0MWoXgFqv4MPZ82q6hutpndfjjnhNLr37MXbr77AGy89zaVXD0vwChyR0iIC6ZVfc58/UPl8lBUR\nqPJcBVLSiZQVkdm2K5vyJ9Hc7MPWZXMJFWwgFouR1rwNBUtmk9OpN5vyJxINlWzrUyZEaXERaZlV\nvncCAWLRaEXZuw49nO+NWJX6cb+Z4/cTjUbYvHY1GVk5nHvLg3z3/muM/+hNDj79IpdWAllpQbaW\n/Opn3QexGGSlVx8rKg2TlR5k9ootHNqrNV/NWsuAjk1p3cRJQJQnuwZ2asZ5+3ViyDPunYwKlxRV\nS0z5/dWfk1Zde8dHYnWaU7hhDWOfuJ2UjCya57l3Uu3Xz0e4yvORnRas9nu5qCxCdlqAOSsLONi0\n4mu7jn4dmpCbU5kQats0jWcv3JOCkhB21VbX1hEuKSbl1z/rVZ6PFl2cUotVf2X9Zo7PTywarUh2\nbVg4m4XffcKB196f+AVU8du/I5W/t6Jlxfir/B3xpaQRLSsmtXUXihb8QvZue1GyYh7hwk3EYjEn\nkVq0heIls2h16AUJiXdn2PW4AeddPd8DKTiN3uusvKRhWD28RERExF33AR8CDwPzga3AQuBR4L/A\n3d6FVl1hifMfM5/PV/udRURERBpOa+Cb7Yx9DOTV9gDGmJ5VP4APq1xOmKKyCJmple8T9/sq9yUL\ny8LVTs5npgYoKA3z3xmrKSqL8MTZ/Tlwt5bY1c7m6rj89eSvKQTg2/x19MjNSmTo1fjTMpzTWOVi\nMXw+ZzswkJZBpLSyV1H5xmXzfofgT01nwesj2ZI/kYw2XfH5fKwc8wrdLriTHpf9H836HMSqr151\nbR2ZmVkUFxVWxhqNVSS7dtTggw6je09ns3m/gw5j4TzbIDHWRSAtk0hp1ecjWvl8pGZWez4iZcUE\n0rJoNeAwAqkZzP7XnWy0E8ls1w2fz0fXE69i5XfvY1+7m2BWM4KZTVxbR1pGJmUllbHGYtFaezxt\na47fHyAjuwm77TkYgB57Dq44+eWWwtIwWVV+nsuTK1D5/6hyWWlBCopDvDdxOUWlYV69Yh+O2L01\nM5dvqbjPcf3bMvyU3lzx0iQ2FbnTvwsgmJ5JqNrXN1brc1LTnKwWrTlh+LPsduBxTH73ucQEvQ2F\npdV/v/p9vornY+uvnqus1CAFJWE+mLKCotIIL16yJ4f1ymX2iso3Dq/aXMpJj4/nnUnLufHYHq6t\nI5ieQbjKzzN1+Bmpac7yKeOY+u7TDB56J6lZ7v2sg/N3JLadvyP+1AyiZVX/jpTgT8ukSZ+D8aWm\ns+yte9g6fxJprbtU7Edszf+Z7N77JWx/wpOEl7V2sbV2//jlfGvtodbaA6y1l1lrdyhz5VcPLxER\nEfGAtTZsrT0H6A9cB4wAhgF7WWvPsta6Vy+hFsUlITLVv0tERETc9x1wwnbGDgQm1uEx/ofzJqOn\ngWcAE//36Zom1de05VvYr6vzrvo+7XKYv66oYmzx+iI6NEsnOy1A0O9jQIemzFyxhd5tc5i0ZBPX\nvjWNr+euY8VmZ4Nw1Bl9MW2c00l7dW5WkQhzQ2ZeTwoW/AJA0fJ80nI7VoyltcyjbNNqIiWFRCNh\nipZZMvN6ULxyPtld+tLt/OE07bUvqc1aAxDMyMGf6rzLPyW7GZGSot9+wgTp3W8PJv74PQBzZk6j\nS7fddmh+1ZNfd95wDflzZgIwddLPdO/Ze3vTGlx2B8PmeZMB2LpsLhmtO1WMZeTmUbphFeH481Gw\nZA7ZHXpSuGIeTbr2o/dFI2ix+2DS4s/HpvzJdDv1eswFdxAu2kLTrv1dW0eHnn2Y/4tzemn5vFnk\nduhay4ztz+lo+lbcvmTOdHI7dE5Q1Ns2edFGDu7l9HYa0LEpc6ucApq/tpDOLTPJSQ+SEvCxV5fm\n/LJkE/06NGX8vPUMeWYCn89YzbINzob/iXu047z9OnLhsxNYscm9E3cAud12Z8Us51fquoVzaNau\n9q/j9uZ8++zdFKx1TkampGXUmqhpSL8s2cxBPVoC0K9DE/LXVD4fC9cV0alFBjnpQYIBHwM7N2Pq\n0s30yWvCTws2cOlLk/ly5hqWbXSej0fP7U/HFs7vrMLSCG6el2nRtTerZztf2w2L5tCkXZffPWfp\nxLEs/P4TDrzmXjJbtE5UyNuV3r4HhQunAlCyYh6prTpUjKW2bE8o/nckFglTstyS3q4HpasWkNmp\nDx3Ovp3snoNIaVoZd9HimWR1GZCweHeGkob1Eoj/wEWi7tQ+FhERESlnjGkBdC7vPWGM6QqcaIxZ\naK2tU1/SRIvFYhSWhMlt5k5fAhEREWncjDFHV7k6GhhljMkC3sLpcdoCOBEYClxWh4fcGye59ZS1\n9ktjzFhr7WENHPZvfJu/nn06N+ef5zpJhPs/y+fIXrmkp/j5ePpqHv96AaPO6IfPBx9PX8X6whCh\nSIwRB/Tiwn07UVAa5oHPnL5G//flPP5yRHdC0RgbCst46Av3TrA06bkPhYums+C1uwDI+8MVbJr1\nA9FQKS0GHEa7wy9g0dv3Qwya9z+UlOzm+AJBln7wOGt+GE0gPYu84y535h47lKUfPIYvEMTnD9D+\nuKGurWO/gw/nl4k/ctPVFwPwp7+N4Jv/fUpJSTHHnHBaxf18bPvEQNXbr/7rbTz96AOkBFNo1rIl\n195wxzbnJELzXoPYsnAas19yPmfXk65i/YzviIZKyR14BB2Pvoi5r99DLAa5Aw8jNac5/kCQ+e89\nyorv3iOYnkXXE68CIL1FO+yrI/GnppHTuQ9Nd9vDtXX03PtAFs6YzCsj/gTA8ZffyMwfviJUWsIe\nh/2h4n5VT3Bsaw7A4eddwX+ff4QpYz4iLSOLk6651bV1AHw5cw3792jJG1cOAuDWd2Zw/IC2ZKQG\neGfCch74eA4v/HFvfMA7E5extqCMUCTG9Uf358rDu7G5OMzt78zA54PbTuzF8k0lPDFkIDFiTFiw\nkSfHzHdlHR0G7McqO4UvRzlf130v+DOLJn5DpKyE7vsfU+WevhrnAOx+1Jn8+NqjBIJBAqlpDDr3\nelfWADBm9loGd2/By390elndOXoWx/ZtQ0ZqgPcnr+Dhz/J5esge4PMxevIK1m0tIxSJcs2Zfbns\n4C5sKQlz1wezAXhh3CLuPmV3yiJRSkIR7vpgjmvraNdvP9bO/YVxj90EwMBz/sSyyd8QKSul8+Aq\nfyZ9Ncw598/EolGmj36ezOa5/Pzi/eCDlt370uuYc11bS9Zue1O0eAbL3hwJQOtjhlIwZzzRUClN\n+x1Kq0POZ8W7DwExmvQ9hGB2M3yBAOs/eYeNP32APz2L1kdX/rkPbVxVLQHW0HxVa6nuQmLlPU1m\nLtrAI2/+wikHdeWkA2p/N0EyaMw9XRrz2qFxr19rb5xrh8a9/sa8doDc3Jyduv6eMaYXTu/RUmtt\n1/htRwCfAYuBw621S1wMKbat75fSsghXjfqGvt1a8Jez3PtPsJv0s9J416+1N861Q+Nef2NeOzTu\n9e/sr43KGWOiONX/thVv+SZU+VjMWhuow2MGccpIrwGO2tGE14EPj9slN7+q+u6GgwA4/cVJHkdS\nP+9e6mxiz13t3umwROnZJpMLXpvqdRj19toFA3h5gpv/bUmMi/dxTsv1uuVzjyOpnzkPOMmpu1xM\njCfKXUf3YMCdY7wOo96mjjiCmz5xr/Rpojx0vOF4F/vKJconVwyCbb/GAJLghFcwXtIwqh5eIiIi\n4q6HgJXAqeU3WGvHGGM645TdeRBw721X21FY4tSNz1JJQxEREXFHg5++ipeK/rMx5mJ2jn70IiIi\nshPa5RNelSUNlfASERERVx0AXPjrU1zW2hXGmLsB9zr71qCoxGkllpm+y7/sExERkV2AtfabBD72\ny8DLiXp8ERER2bXt8jsfgYBzwisSUcJLREREXOUHUmsY3ymaZhWVOgmvLCW8RERExAPxHqcH47xu\nKi9B5AeygEOstSd5FZuIiIgkl11+5yMQL2kYjkY9jkREREQamfHAjcaYT621JeU3GmPSgGHxcc+V\nlzTMTFNJQxEREXGXMeYs4DWc/aeq/bvKL8/yIi4RERFJTrt8wssfT3ippKGIiIi4bDgwDlhojPkc\nWA3kAscAzXHeyew5lTQUERERD90CTAKuBq4FAjh9To8H7sV5k5CIiIhIg9jlG32Wn/BSSUMRERFx\nk7V2IjAY+AH4A3ADcAowETgwPu65whKVNBQRERHP9AIestZOAcYC/ay1s621DwNPArd6Gp2IiIgk\nlV1+5yMQcHJ2UZ3wEhEREZdZa6cCp29rzBjTzVq7wOWQfqOovKRhukoaioiIiOtiwPr45XlAb2OM\nz1obAz4BzvcsMhEREUk6dT7hZYx5Md5odFtjxhjzUcOFVXfBipKG6uElIiIi3jLG+IwxJxpjPgXm\neh0PVJY01AkvERER8UA+sGf88lwgHegTv54Z/xARERFpEDXufBhjOlW5ehEw2hgT2cZd/wAc2ZCB\n1VVAPbxERETEY8aYXOAy4HKgExAGRnsaVFyheniJiIiId14F7jPGBK21DxtjfgT+aYx5HKe/13Rv\nwxMREZFkUtvOx1PAsVWuv7+d+/mAMQ0S0Q4qL2moHl4iIiLiNmPMAThN2E8DUoGZOM3XX7fWrq9p\nrlvKSxpmqaShiIiIuG8U0AroH79+LfAp8BawGTjRo7hEREQkCdWW8LoKOA4nofVP4CFg4a/uEwE2\nAp83eHR14PfphJeIiIi4xxiTCQzBeZ3UD2ez5nXgEuBaa+23Hob3G4WlYXw+SEsNeB2KiIiINDLx\nXl1/q3J9sjGmO9AbmGOtLfAsOBEREUk6NSa8rLVLgGcAjDFtgOestSvcCKyuAgEn4RVWDy8RERFJ\nsHj5nSFADvAVcAHwHpABXOphaNtVVBImMy1Y8SYhERERES9Za7cCE7yOQ0RERJJPnZs5WGtHGGP8\nxpgm1totAMaY84HOwPvW2tmJCrIm5T28ojrhJSIiIol3DTAVuNxaW7FRY4xJ9y6kmhWVhFTOUERE\nRFxjjFkJ1HWTJmatzUtkPCIiItJ41DnhZYzpDfwXp2TP7caY+4Gb48N3GGOOttaOS0CMNSpPeKmH\nl4iIiLjgUZxTXT8aYyYCLwL/9jakmhWVhGnWKs3rMERERKTx+Jy6J7xEREREGkydE17AfUAJMNoY\nkwpcCfwHuBx4CbgHOKTBI6yFz+cj4Peph5eIiIgknLX2L8aYm4FTgcuAJ4FHcN4UFGMn29wJhaOU\nhaNkpe/ISz4RERGR389ae7HXMYiIiEjj5N+B+x4M3GqtnQgcBjQBnrXWbgaeBvZMQHx14iS81MNL\nREREEs9aG7LWvm2tPRroBowCBgM+4N/GmP8zxgz0NMi4opIQABkqaSgiIiIiIiIiSW5HEl7pwIb4\n5eOAUqC8hKEfCDdgXDvE7/eppKGIiIi4zlq7xFo7HKen6fHAz8D1wERjzExPgwMKS5yXZzrhJSIi\nIiIiIiLJbkd2P+YCpxljLHAGMNZaW2aMCQJXAzMSEWBdqKShiIiIeMlaGwM+BT41xrQGLgYu9TQo\noKjUSXhlKuElIiIiIiIiIkluR3Y/HgReA67F6U9xYfz2uUAecHLDhlZ3gYCfsBJeIiIishOw1q4B\nHop/1Fk8UTYROBKIAC8DUWCGtfaa3xNLeUnDLJU0FBEREREREZEkV+eShtbaN4FDgL8B+1trv4oP\nPQccZK39LAHx1UnA7yOqHl4iIiKyi4qfmH8aKIrfNAqnd+ohgN8Y87veWFRe0lAnvEREREREREQk\n2e3Q7oe19nvge2NMpjGmLbDeWnt/YkKrO5U0FBERkV3cw8BTOG8s8gF7WmvLe6V+ChwFfLCjD1pU\nnvBKU8JLRERE3GGMOXpH7m+t/SJRsYiIiEjjskO7H8aYA3A2ZPbB2YyJGWN+BP5WZVPGdYGAn1C8\nR4WIiIjIrsQYczGwxlr7pTHm1vjNVU/hFwBNf89jF6qkoYiIiLjvM5xWGL4a7lM+HgMCiQ7ouxsO\nSvSncM27l+7ldQgNomebTK9DaBCvXTDA6xAaxMX7dPI6hAYz54FjvA6hQdx1dA+vQ2gQU0cc4XUI\nDeKh443XITSIT64Y5HUICVfnhJcxZl9gDLACJ+m1Eqd315nA/4wxB1lrf05IlLUI6oSXiIiI7Lou\nAaLGmKOAAcArQG6V8RxgU10eKDc3p/oNfidvlteuyW/Hkkyyr682jXn9Wnvj1ZjX35jXDlr/LuAw\nrwP4tV63fO51CPVWvol/+ouTPI6kfsoTdv+estzjSOrv3IF5SbOOu77I9zqMeitPEP3f1ws8jqR+\nbjy0GwA3fWI9jqT+Hjre0HboO16HUW+rnjuD96et8jqMeju1f1s6XrPDhWN2OkufrLnjw46c8Lob\nmAAcYa0tK7/RGHMH8D/gLuAPOx5i/fn9PiLq4SUiIiIeMsb8pjeqtbbWFyjxPl3lj/EVcCXwf8aY\ng6213wLHAV9tb35Va9cWVLu+boPTEqysuOw3Y8kkNzcnqddXm8a8fq29ca4dGvf6G/PaoXGvf1dJ\n9Flrv/E6BhEREWmcdiThtR8wpGqyC8BaW2qMGQW82KCR7YCA30ckohNeIiIi4h5jTGvgH8BJQPo2\n7hJjB8tHV3ED8JwxJgWYDfyut8WVlzTMVElDERER8YgxpitwMJBKZZlDP5AFHGKtPcmr2ERERCS5\n7MgmTHENY/XZ0Km3QEAlDUVERMR1/wBOAd4GlgH1Pm5urT28ytVD6/t4JWURANJTE94aQ0REROQ3\njDFnAa/h7BmVb9z4qlye5UVcIiIikpx2JEn1M/AnY8xH1tpI+Y3GmAAwDPipoYOrq4DfTyQaIxaL\n4fPV1BNVREREpMH8AbjBWvuk14FsTygcJeD3EQz8ptqiiIiIiBtuASYBVwPXAgHgQeB44F6c/SQR\nERGRBrEjCa87gB+BOcaY/wCrgLbAmUAn4PAa5iZUwO8kuaKxGAElvERERMQdMZxygzutUDhKMKhk\nl4iIiHimF3C+tXaKMWYsMMxaOxuYbYxpD9yK0xdeREREpN7qvANirZ0CHA2sB24GHo3/uw44xlr7\nfUIirINAwElyqY+XiIiIuOhTnP5dO62ycIRUJbxERETEOzGcfSSAeUBvY0z5O5U/Afp4EpWIiIgk\npTqd8DLGZAPNrLXfAIONMZlAM+Bk4A1r7eYExlir8lNd6uMlIiIiLvoP8JwxpjXwA1D06ztYa190\nPaoqQuEoKUp4iYiIiHfygT2Bb4G5QDpOkmsGkBn/EBEREWkQte6AGGNOBBbj1FsGwFpbhPMunSeB\nxcaYoxMWYR0E4n0plPASERERF70DNAfOAR4Dnv/Vx3PeheZwEl4Br8MQERGRxutV4D5jzA3W2g04\nrTL+aYw5E7gLmO5lcCIiIpJcajzhZYwZgPPu5ek4R82rWo3TrP0e4ENjzF7W2pkJibIW5T28lPAS\nERERF/XwOoDalIWjNMnSCS8RERHxzCigFdA/fv1anLLQbwGbgRM9iktERESSUG0lDW8BpgEHWWtL\nqw5Ya6PAZ8aYb4EJ8fsOSUiUtajs4RX14tOLiIhII2StnV9+2RiTBuQAG+KvkXYKKmkoIiIiXrLW\nxoC/Vbk+2RjTHegNzLHWFngWnIiIiCSd2nZA9gMe+3Wyq6p4ecN/AAc0ZGA7Qie8RERExAvGmMHG\nmHHAVpzT76XGmK+NMft7HBrRWIxwJEqqEl4iIiKyE7HWbrXWTlCyS0RERBpabSe8WgNL6vA4c4G2\n9Q/n9wn41cNLRERE3GWM2QcYC6zBefPPSiAPOAP4yhhzgLV2klfxhcLOQbOgEl4iIiLiEWPMSpwe\n8NtlrW3vUjgiIiKS5GpLeK0COtbhcdoBa+sfzu9TccJLJQ1FRETEPfcAk4EjrLUl5TcaY24FxgAj\ngBM8iq0i4ZUaDHgVgoiIiMjn/DbhlQPsC6QBf3c9IhEREUlatSW8vgIuAV6v5X6XAJ69g1klDUVE\nRMQD+wEXVU12AVhrS4wxjwAveBOWozzhpR5eIiIi4hVr7cXbut0Ykwp8iJP0EhEREWkQte2APA4c\nZIz5hzEm/deDxph0Y8yjwBHAk4kIsC4CASW8RERExHUlwPaOl0eo/Y1FCRUKRwAlvERERGTnY60t\nwykJfZnXsYiIiEjyqHEjxlo71RhzDfBP4FxjzBhgIRAAugKHA82AW6y1YxId7Paoh5eIiIh4YAJw\nnTHmI2ttReLLGOMH/hQf90xZRUlDJbxERERkp5QFtPA6CBEREUketb7z2Fr7vDFmOnAzcDJQftJr\nC/Ap8Ii1dmLiQqydeniJiIiIB4YD44FZxpi3cXqftgXOArrhnID3jEoaioiIiNeMMUdv4+YATr/4\n24Cf3I1IREREklmdSu1Ya38CTgMwxrQCwtbaTYkMbEeopKGIiIi4zVo7yRhzLPAQcHuVoUnAcdba\ncd5E5qhMeAW8DENEREQat8+AGODbxthiYJi74YiIiEgy2+HeEtbadYkIpD4qTngp4SUiIiIustZ+\nBextjMkBmgMbrbUFHocFQJl6eImIiIj3DtvGbTGcqkFTrbXayBEREZEG42kz9YZS0cMrotdJIiIi\nkjjGGH95v654r65yhfGPardX7e3ltpB6eImIiIj3YsBka+3WXw8YY5oZY4611r7pQVwiIiKShJIk\n4VV+wks9vERERCShQsaY/ay1PwNhnE2c7Ynh4WstJbxERERkJzAWGAxM2MbYQOAlYKdNeN15Sm96\ntcuhNBzl9ndnsmxDccXYYb1zuerw7oQjUd6btJx3JiwnJeDjvjP60rFFJgUlYUZ+MIulG4rp1S6H\n207qRSQaoywc5ea3p7OxMOTKGmKxGCu/eJHiNUvwB1PIO24oqc325nDuAAAgAElEQVTaVIxvmTeJ\ntT+8j88fpFm/Q2gx4DCikTDL//s0ZZvWEEjLpN1Rl5DWvA3Faxaz4vMX8PmDpLVoS95xl7uyhvJ1\nfPLCo6xaPJ9gaionXX4DLdq0r3afstISXr3vJk6+4kZate+43TnvPHY3WzdvhBhsWruKDj1254zr\nb9/OZ9Y6treOiW//k03LF+IPprLvedeT3apttfuEy0oY++Rw9j3/TzRpnbfdOZtXLmHCm08AkN26\nPfueez0+v3v/h4nFYnz/xhNsWLaQQEoqBw35E01y2/1mLZ8+ehsHXzSMpm061Dpn3s9jmTX2I066\neZSr65j27lNsXrGIQDCFPc6+jqyWv35OShn/zHAGnn092fHnZFtzNi9fwPT3n8XnD+APprDnecNI\ny27q2loePH8gu3doRmk4wl/+NYkl6worxo7q346/nNCbUCTKm98v4o3vFhHw+3js0n3o2DKTSCTG\nX1+dxILVW+nbsRkPXbAnpaEIM5Zu4o63prq2hlgsxujnRrFy8XyCKamcftVNtNzGz/oLd9/AGVff\nTG78Z31bc1YsmsfoZx/BHwzSql1HzrjqJtfWUe6+c/rTO68ppaEIN73+C0vWF1WMHdm3DX86zhCK\nRHn7xyW8+cMSUgI+HhkykE4tsygoCXH7W9NYvK6Izq0yGTVkT6KxGHZFAbe/Pa3BY02KHRD18BIR\nERGX3Acsq3K5po/7vQiwXFnISXgFlfASERERFxljXjHGfGWM+Qqnd9dT5derfgCvAqu9jXb7juzT\nmtSgn3Of+plRn+Vzy/GmYizg93Hz8YZLnp/Ahc9O4KxBHWmelcKZ+3SgsDTCOU/9xL0fzWb4ybsD\ncOuJvbh79Gwufm4i/5u5hssP7ebaOgryJxKNhOk+ZARtDjmHlV+9VjEWi0ZY9dVrdDn7Vrqeezsb\np44hXLSFjVO/wp+aQfchI2l35EWs/PIlANZ+/x6tDzidbucPJxoOUTB/imvrmDPhO8LhEJfd/QRH\nnjOUz199qtr4igVzeXnEMDauWVnrnDOuv4OL7xjF2X8dSXpWNsdedI3WsYOWTRtPJBziqL88zICT\nLmLye89XG9+wZB5j/vE3CtetqnXOtI9fYcDJF3PksIcgBstn/OzaOgAW//ID0XCIk24exT6nXsxP\n/3mu2vi6xfl8/PBNFFRZS01z1i2Zx9zvv3At/nIrp/9INBzi4OsfovfxFzLjgxeqjW9aOo/vn/wb\nhetX1zpn+ujn6X/alRxw9b206zeY/DHvuLaO4wa2JzXo58QHx3LvezMYcVb/irGA38eIs/pz5qhv\nOe3hbxhycDdaZqdyRL+2BHw+Tnrwa0Z9MptbT+0LwMMX7sltb/7CqQ9/Q0FxiFMHdXRtHTN/Hkc4\nHOLqe//Jsedfzif/erLa+LL5lmfvvJ4Na1bUOmfMf17myLMu4cqRjxMOlTFn0njX1gFw7IB2pAb9\nnPrIOB74cDbDT+9bMRbw+xh+el/OffwHznr0e84/oAstslM574AubC0Jc8oj4xj+n+ncc7bzPA4/\nvS8PfjiLMx/9Hr8fju7fdnuf9nfbKU54GWOCwL+ALjjvlh5qrZ1b1/nq4SUiIiJusNbeUeVyjW+f\nNMa0q2k80UKR8hNeAS/DEBERkcZnNHBj/HIMSAMyfnWfCDAFeHRHHjheOrodsDLRpaP36tKccfE2\n9tOWbqZvh8rTDd1zs1i8rojCUqdn6qRFGxnUtQXd22Tz7dy1ACxaV0S31lkADHtjKuu3lgHOHlZJ\nKJLI0KspXGbJ6ToAgMz2u1G8cmHFWOn65aQ2b0sgLdMZ79CLwiWzKV23nJxuzpy0Fu0oXe9syKa3\n7kykuIBYLEa0rAT87r3OXGJnsNuAQQB06NGbFQtstfFIOMQ5N4zkvSfvr/Ocr//zMvseeyrZTZsn\nOPpKybKOtfNn0b73XgC06mLYsDS/2ng0EuKgobcx/pVRtc458LLb8Pl8RMIhSrZsJCUj06VVOFbN\nm0WHPnsD0LprL9Yurr4lHQmHOeqq4Xz90sO1zinZuoVJH7zCfmdfybhX/+HSChwbFs6idS/n69ui\ns2HT0nnVxqORMIMuuY3Jb4za/pxl8wHY+8IbSc9xvp9i0SiBlDQ3lgDAvru1YuxMJyk3ZeEGBnSp\n/L7u0S6HhWu2srUkDMBP89YxuGcudsUWgvEDMU0yUiiLVztp1yyDKQs3ADBh/nqOGdCe939e6so6\nFs2ZjtnD+bnt1GN3ls3/7c/6kBvv5a3H793unOXxn/X2XXtQWLCZWCxGaXER/qC7KZ19urfg61lr\nAPhl0Ub6d2pWMdajbTYL1xZWPCc/z1/P4B4t6dE2p2LOwjWFdG+TA0C/js34eb7znIyduYaDeuXy\nxbRVNKSdIuEF/AEIWGsPMMYcifOu6DPqOlk9vERERMRtxpgyYH9r7cRtjB0E/BfIcT2wuFB8IyVF\nJ7xERETERdba94D3AIwxC4ELrLW/u46UMeYFa+0fjTH7Aq8D64EcY8yl1tofGyTobchKC1Zs4IHz\nJmufD2IxyEqvPlZUGiYrPcjsFVs4tFdrvpq1lgEdm9K6ibNJXJ7sGtipGeft14khz7h3giVaWow/\nrTLf6PP7icWi+Hx+IqXFFckuAH9KOtGyYtLbdKFg3hSa9NibouX5hLZuJBaLkdq8LSu/fJm14z/A\nn5ZBVqfdXVtHaXEh6ZlZlbH6A0SjUfzxPcGOPfs4A7G6zSncsomFM6e4eiqqtphg11lHuKSoWmLK\n7w8Qi0YrShG26to7PhKr05zCDWsY+8TtpGRk0TzPvROQAKGSIlIzqnx9A9XX0qZ7fC2xWI1zIuEQ\n4159lH3PvJxAMKXa/d0QLikmJb3y6+v71XPSoksvZyBWwxyfn1g0WpHs2rBwNgu/+4QDr3WveEl2\nepAtxZUlXyORyt+9Oekp1cYKS8I0yUihsDRMx1ZZfHf3MTTPSmXIE98DsGhtIfv2aMVP+es4ekA7\nMtPcS9I7P7fZFdf9geo/651N+Smp2Hbn+OI/663adeCD5x9l7Luvkp6ZRbfd93BlDeWy04MUVPm6\nh6v8PcxOT6k2VlgaJic9hRnLNnFE3zZ8MW0VA7s0p22zdHw+8PkqH3drSZicjJQGj3dnSXjNBYLG\nGB/QFCjbkcmVJQ3Vw0tEREQSxxjzZ6D8fwRB4FJjzNHbuOuBgDuNGbaj8oSXEl4iIiLiDWttV2NM\nK2PMSdbaDwGMMd2AU4CXrbUb6vAwXeP/3gscZ63NN8a0B/4NHJKQwHE27bLSKrfNyjf3wNlkzUqv\nHMtKczYDx8xaw26ts3n1in2YsmgTM5dvqbjPcf3bcvmhXbnipUlsKnLvZaI/LcM5jVUuFsPnc14f\nBtIyiJRW9iWLlhXjT8ukSY+9KV23jAWvjyQzrwcZbbri8/lYOeYVul1wJ2kt81g/+UtWffUq7Y+6\nxJV1pGVkUVpc2TMmFotVbBz/njmzfvyGfgccga/q7qsLkmUdwfRMQiWV3zuxWKzWvls1zclq0ZoT\nhj/L/PFfMPnd5xg8ZFhiAt+GlPRMQiVVvr7R2teyrTkbli1ky5qVfP/GE0TKStm0aik/vv0sg89y\np9ddMD2DcJWfZ2LROjwn25+zfMo45o55h8FD7yQ1q0kiQt6mrSVhsqv97vVV/O4tKAmRk16ZIMlK\nD7K5qIwrjuzB2BmreGD0TNo2S+fdGw7h0Du/YNi/JnLP2XsQOMHHT/nrKM1wL3fwm5/bKsmuHZ3z\n0UuPc+U9T9A6rzPjP3+fT/71JCdf9ueExf5rW3/1N89f5e/h1pIQ2VXGstOCbC4K8cW0lfRsm8M7\nww5g4oKNTF+yiVgMqhbo+3Vys6HsLDsgW3FewMwBngEe25HJKmkoIiIiLmkG3BP/iAFXVrle9eNQ\n4EFvQnSU9/DSCS8RERHxijFmd2AGULW2VxfgAWCiMabLDjxcxFqbD2CtXUGC97QmL9rIwb1aATCg\nY1PmrtpaMTZ/bSGdW2aSkx4kJeBjry7N+WXJJvp1aMr4eesZ8swEPp+xmmUbnI3kE/dox3n7deTC\nZyewYlPJNj9fomTm9aRgwS8AFC3PJy23sodNWss8yjatJlJSSDQSpmiZJTOvB8Ur55PdpS/dzh9O\n0177ktqsNQDBjBz8qc5psZTsZkSqbPgnWkfTh/xffgJgaf4s2nTqWsuMmucsmDGZ3eKlw9yULOvI\n7bY7K2Y5hS7WLZxDs3adf/ecb5+9m4K1TtnMlLSMWpM0Da1N991ZOmMCAGsWzKZFXpffNSe3S09O\nv/Mpjv/LAxw29BaatevkWrILoEXX3qye7Xx9NyyaQ5N2XX73nKUTx7Lw+0848Jp7yWzROlEhb9PP\n89ZxRD+nr9Oe3VowZ/nmirH8lQV0aZ1Nk4wUUgI+BvdoxcQFG9hUVFZxymhLUYig30fA7+PIfu24\n6vmfOPvv42iRnco3s9xrG9mlVz/mTHEOIS+ZO5O2nWo/ubi9OZnZTUiLn8Rr0rwVxUVbt/sYiTBh\n/gYO79MGgIFdmjNnReWbOfJXbaVLbjZNMpy/h4N2a8nkhRsY0Lk539l1nPH37/lk8nKWrHP+XsxY\nuol9d2sJwGF9WvPzvPUNHu/OcsJrGPCZtfY2Y0weMNYY09daW6eTXhUJL5U0FBERkcQaAfwfTgP2\nLcBhwK9LGkaste7uZGxDKFye8FIPLxEREfHMg8BSnBNdAFhrvzLGdAQ+io+fXctjNDXGTAKyjDF/\nxClr+AiwODEhO76cuYb9e7TkjSudZMKt78zg+AFtyUgN8M6E5Tzw8Rxe+OPe+IB3Ji5jbUEZoUiM\n64/uz5WHd2NzcZjb35mBzwe3ndiL5ZtKeGLIQGLEmLBgI0+OmZ/I8Cs06bkPhYums+C1uwDI+8MV\nbJr1A9FQKS0GHEa7wy9g0dv3Qwya9z+UlOzm+AJBln7wOGt+GE0gPYu845xN+7xjh7L0g8fwBYL4\n/AHaHzfUlTUA9N7nIBZMm8QLw68D4OQrb2L692MoKy1hr8OPr7yjr+Y55davXEbz1u633E2WdXQY\nsB+r7BS+HOW069v3gj+zaOI3RMpK6L7/MVXu6atxDsDuR53Jj689SiAYJJCaxqBzr3dtHQBdBu7P\n8tmT+fChvwJwyEXDmP/z14TKSuh14LGVd6xyim5bc7zWrt9+rJ37C+Mec74/Bp7zJ5ZN/oZIWSmd\nB1cpSuKrYc65fyYWjTJ99PNkNs/l5xfvBx+07N6XXsec68o6/jtlBYfs3oYPbz4UgD+/PJFTBnUk\nMzXAG98t4q63p/LWsIPw+eCN7xaxZnMJz36Zz98v3pv3bzyElICf+96bQUkoysI1W3nnrwdTVBrh\ne7u2ojeYG/oMOoj8qRN46nan3OgZV9/CL9/9j7LSEgYdcUKVe/pqnANw+lU38cbfRxAIBggEUzj9\nihtx02dTV3Jw71ze+8uBAPz1tSmcvFceGWkB3vxhCSPfncHr1+6Pzwdv/rCENVtKKYtEueGEvbnu\n2J5sLirjxtedN17c/d5MHjpvD4IBH/NWbeWTKSsaPF5fzOV6ottijLkNCFlrHzLGZAHTgT7W2uLt\nTKkW9M8zV3H3iz9x6Yl9OPXQ3RIdroiIiLjD3bocO8gY0x1YYq31tHRhFbG1awsqrrz+xVzGTF7G\nyEsH0aF1dg3Tdn25uTlUXXtj05jXr7U3zrVD415/Y147NO715+bm7NSvjbbFGLMeGGKt/e82xk4C\nnrfW1np8wBiTBgwAinDaYlwKvFDX12G9bvnc+82veprzgJNEOP3FSR5HUj/vXroXAP+estzjSOrv\n3IF5SbOOu77I9zqMervr6B4A/N/XCzyOpH5uPNQ51XPTJ9bjSOrvoeMNbYe+43UY9bbquTN4f9oq\nr8Oot1P7t6XjNR94HUa9LX3yZKhhv2hnOeH1KPCiMeZbIAX4Ww3JLoBqL3C3bnXeRL15S3GjeOHb\nyF/gN9q1Q+Nev9beONcOjXv9jXnt4Kx/Z2atnW+M6WSMOQhIpfIFlx/IAg6x1p7mVXxl4QigkoYi\nIiLiKT+Qtp0xH5Belwex1pYCP1e56el6xiUiIiJJaKdIeFlrC6n9CPt2qYeXiIiIuM0YcwZOSZ0U\nKk+f+6pcnutFXOVCEfXwEhEREc/9ANxsjPncWlvR8MkYkw78NT4uIiIi0iB2ioRXfamHl4iIiHjg\nb8AvwLXA1TjvYH4YOB4YGb/NM6GQk/BKTVEPLxEREfHMHcB3wCJjzBfAaiAXOApoChzoYWwiIiKS\nZJLiLb8Bv7MMnfASERERF/UGHrTWTgDGAH2ttdOttQ8A/wRu9zK4snD8hFcgKV7uiYiIyC7IWjsZ\nGASMBQ4HrgOOxTnZNRhovPW7RUREpMElxwmvQHlJw6jHkYiIiEgjEgPWxi/PA3obY3zW2hjwEXCO\nZ5EBIfXwEhERkZ2AtXYGVdpYGGMCwMnAIzhJMB1HFxERkQaRHAkv9fASERER980DBgLjgHycpuu7\nAzOBDCDLu9AgFI4S8Pvwx18niYiIiHjJGNMGuBwYCuQBZcB/PA1KREREkooSXiIiIiK/z+vA/caY\ngLX278aYn4EnjDGPAbfiJL48EwpHSU3R6S4RERHxljHmEJzepqfg7ENNAx4A3rDWbvIyNhEREUku\nyZHwivemiESU8BIRERHXPIzTdH3v+PVrgU+Bd4EtwEkexQU4PbxSgqoQJCIiIu4zxmQDFwFX4fQ9\n3QC8hHO660/W2m89DE9ERESSVHIkvPzq4SUiIiLustZGgRurXJ9ojOkO9AFme/2O5VA4QkpAJ7xE\nRETEXcaYp4DzgUzgS2AEMBqn3PPlHoYmIiIiSS7JEl464SUiIiLesdZuAcZ7HQc4JQ2zMlK8DkNE\nREQanyuAqcDl1toJ5TcaY7RpIyIiIgmVXAkvlTQUERGRBDLGLAXq/ILDWtspgeHUqCwcpZlOeImI\niIj7HgaGAD8aY34BXsTpfapNGxEREUmopNgFqejhpRNeIiIikljfVPn4FmiDU57nW+AtYAzO66uW\nwPsexQg4J7xSUpLipZ6IiIjsQqy1NwEdgTOB1cA/gBU4ia8YSnyJiIhIgiTVCa+oEl4iIiKSQNba\nC8ovG2NGAFOAo+OlDMtvzwA+BjyrJxiNxohEY+rhJSIiIp6w1oaB94D3jDF5wB+BiwEf8LYx5k3g\njaolD0VERETqKyl2QcoTXuFo1ONIREREpBG5CrivarILwFpbDPwdONeTqHBOdwGkpgS8CkFEREQE\nAGvtcmvtSGttN+AYYBzO66gfjTHW2+hEREQkmSRHwiugHl4iIiLiuhQgcztjrYCIi7FUUxZ2PnVK\nMCle6omIiEiSsNZ+aa09C8gDbgRCHockIiIiSSQpShr6ffGEl0oaioiIiHv+B9xnjJlprZ1efqMx\nZjBwL/CRV4GVn/BSwktERER2Rtba9cCo+IeIiIhIg0iKhJfP5yPg9xFRSUMRERFxzzDgO+AXY8xi\nYC3QFugATAf+4lVgFSUNlfASERERERERkUYiaXZBAn6fShqKiIiIa6y1y4DdgeuBicBWYDxwObCP\ntXajV7GVlZ/wCqiHl4iIiIiIiIg0DklxwgucPl5RlTQUERERF1lri4An4x87jYqShilJ894mERER\nEREREZEaJU/Cy+9XDy8RERFJKGPMpcAH1tr18cs1sta+6EJYvxEKRwCVNBQRERERERGRxiOJEl4+\nwkp4iYiISGI9D8wA1scv1yQGeJLwqihpqISXiIiICHMeOMbrEBrMu5fu5XUIDeLcgXleh9AgkmUd\ndx3dw+sQGsyNh3bzOoQG8dDxxusQGsSq587wOoQGcWr/tl6H0CCWPnmy1yEkXPIkvAI+IpGo12GI\niIhIcusBLK1yeadUUdIwqB5eIiIiIiIiItI4JE3Cy+/zqaShiIiIJJS1dv62Lu9syuIlDXXCS0RE\nRAQyBl7rdQj1VjzlCQCanPOKx5HUz5Y3LwRgwJ1jPI6k/qaOOIL3p63yOox6O7V/W276xHodRr2V\nn4ja1b+3po44AoDr3p/tcST19/ipvbnsrRleh1Fvz5/dl7mri7wOo956tsmkoHTXPzCUk1bzPkfS\nJLwCAT9l4bDXYYiIiEgSM8aM3IG7x6y1dyYsmBqUn/BSDy8RERERERERaSySJuEV9PuI6oSXiIiI\nJNbtO3DfGOBpwksnvERERERERESksUiahFfA7yMS3fWP5ImIiMhOLcXrAOqiLKSEl4iIiIiIiIg0\nLsmT8Ar4iER0wktEREQSx1obqet9jTGBRMZSk1CkvKShZyGIiIiIiIiIiLgqeRJefj8RlTQUERER\nFxljzgMOA1IBX/xmP5AF7Ae09SKuUNjJy+mEl4iIiIiIiIg0FkmU8PIRicaIxWL4fL7aJ4iIiIjU\ngzFmOHAXsBUIACEgAjQHosDzXsWmHl4iIiIiIiIi0tgkTcLL73eSXJFojGBACS8RERFJuAuBN+L/\njgTyrLWXGGMGAx8DU+ryIMYYP/AcYHASZVcCpcDL8eszrLXX7EhgZeHykoZKeImIiIiIiIhI45A0\nuyCBeJIrqrKGIiIi4o6OwCvW2ihOcmt/AGvtj8D9wOV1fJwTgZi19kDgDuA+YBRwq7X2EMBvjDl5\nRwILhXTCS0REREREREQal6TZBQn6naWoj5eIiIi4pBinjCHAfKCbMSYtfn0C0K0uD2Kt/YDK5Fhn\nYCOwp7V2XPy2T4EjdySwUKQ84RXYkWkiIiIiIiIiIruspEl4BaqUNBQRERFxwS/AafHLc+P/HhD/\ntzNOOcI6sdZGjTEvA4/hlEmsWp+5AGi6I4GVhSKATniJiIiIiIiISOORND28yksaRiJ13lsSERER\nqY+/A+8bY5paay80xowG/mWMeQ84FxhX8/TqrLUXG2Na45wOy6gylANsqstj5ObmAOALOImu9u2a\nkpbSOE55la+9sWrM69faG6/GvP7GvHbQ+kVERERk25In4aUTXiIiIuIia+0HxpiTgN7xm64E3gSu\nAH4CrqvL4xhjLgA6WGsfAEqACDDRGHOItfYb4Djgq7o81tq1BQAUFpYBsHljIT6fr6YpSSE3N6di\n7Y1RY16/1t441w6Ne/2Nee3QuNevRJ+IiIhIzZIm4eWPJ7zCSniJiIhIghhjsqy1heXXrbUfAx/H\nL68HjvodD/se8JIx5huc12bXA3OA540xKcBs4J0decCycJRgwN8okl0iIiIiIiIiIpBECa+A3ynd\no5KGIiIikkArjDH/Bp611k5uiAe01hYBZ29j6NDf+5ihcJRU9e8SERERERERkUYkaXZCynt4RXXC\nS0RERBLnfeB8YIIxZrIx5gpjzE5XXygUjpCihJeIiIiIiIiINCJJsxOiHl4iIiKSaNbai4G2wFCg\nAHgK59TX88aYQV7GVlUoElXCS0REREREREQalaQpaRgsL2mohJeIiIgkULyH14vAi8aYbsAlwBDg\nEmPMDOBZ4DVr7WavYiwLRcnJTPHq04uIiIiIiIiIuC5p3vpbXtIwElHCS0RERNxhrV1grb0D6Aoc\nA0wH7sc59fWSV3E5PbwCXn16ERERERERERHXJU3Cy+8rL2kY9TgSERERaWystTFr7f+stRcApwNL\ngAu9iicUjpKSkjQv80REREREREREapU0JQ3LT3iFVdJQREREXGaM6YOT4DoPaA9MAq70IpZwJEo0\nFiMloISXiIiISH3849az6d8zj5LSEFeNfINFy9dXjP3h4L78beixhMIRXvnwR15+fzyBgJ/nRw6h\nc/sWhCNRrh75b+YtWVMx58G/noZduJoX3/ve1XWM+uO+9OvcnJKyCNc9O55Fa7ZWjB27ZwduPq0/\noUiU176exytj5xHw+3jm6gPolJtNOBLl+ufGM29lAS9edxC5TdPx+Xx0ys1iQv46/vj4ONfWcdsJ\nhp5tsikLR7nrw9ks31hSMXZIz1ZcfkgXQtEYH0xZyfuTVxAM+Bh5yu50aJ7B1pIw931iWbaxmG65\nWdxxYi8Alqwv4q4PZxNzaTsxFosx+rlRrFw8n2BKKqdfdRMt27Svdp+y0hJeuPsGzrj6ZnLbd9zu\nnBWL5jH62UfwB4O0ateRM666yZ1FxNcx7d2n2LxiEYFgCnucfR1ZLdtWu0+4rJTxzwxn4NnXk906\nb7tzNi9fwPT3n8XnD+APprDnecNIy27q2logeb63Zo9+hoKVC/EHU+lz+jVk/uo5iZSVMumFu+hz\nxrVk5ebVOsd+/CJZuXl02PcYdxYRX8e8j5+lcNVi/MEUepx8FRktfruOGa+MpMcp15DZqv125xRv\nWMXc954An4+sNp3Y7YShrq7jqVH3sXDeXFJT07ju5uG0bd+h2n1KSooZ/ter+dMtd5HXsXPF7XbW\ndP71zGPc94/nAFiQbxl5y/XkdXDuc9wpZ3LgYUe5upYH7hlB/lxLamoat4+4mw4dOlZfS3Ex11x5\nGcNH3EPnLl0Jh8OMHH4bK1csJxQKcenQKzn40MNYtnQJd91+K36/j+679eDm24Y3eLxJsxMS8DsJ\nr6gSXiIiIuICY0wbY8wwY8xkYBowFBgN7GmtHWStfc6LuEJh57R7ajBpXuaJiIiIuO6kw/qT9v/s\n3Xdg1PX9x/Hn7eyQBRnsdewhynIgKoKjTqqiImodiAW17l3FUW1tVdwKIlr150JrsXWAdYLsDUcg\nzAxIIIvkcvv3x4VLIgIqyR3evR7/tHefe1/e79w35/F53+fzsZgZefnfuW/av3j85vNCYyaTkcdu\nPo/TJz7DqVc/xR/OO5bMtCTGHNcbk8nISVf8g0df/i8PTv4dABmtEpk97TpOP6FP2Os485h22Mwm\nRt33Xx54exmPjD+6oQ6jgUfHH81ZD3/O6Q9+yhUndycj2capA/MwGQ2cev9/efyDVdx30VEAXDnt\nG3730Odc8sSXVNS4uf21RWGr46QeWVhNRiZMX8LTX2zilvkvoqIAACAASURBVNHdm9Rx85huXDNr\nGVe9upSxg3JJS7Bw/qA8al1eLntlMY/9x8FdZ9gBmHxyZ576fCNXzFiCwRBsaITLmoXf4PV6mPTw\nc4y55BrmvPZsk/Edmxy8dP8U9uwqOmTM3HdncsoFVzDxwWl4PW7WL5kftjqKVy3A7/VwwpTH6XnG\nZaz+aHqT8YrtG/nu2Tup2b3zkDGrPnyFfudN5NhJD5PTdyj5c98LWx0QPdfWrjU/4Pd6GDLpMbqN\nGY9jzowm41U7NrLopbtx7ik5ZIy7poqlrz5I6brw/Y3vs3vdQgJeLwOufoSOp1zC5v/ObDJeXbSJ\nlTPupa585yFjCv47k46nXEz/P0wlEPCze93CsNWx4Jsv8Xg8/PX517js2sm88swTTcY3OtZy55Sr\n2Fm0o8n9H7z1Gs88PhWP293ksedcOJ6Hn3qJh596KazNLoD/zfsCt8fDjNff4o833MQ//vpYk/F1\na9dwzZWXUbhje+i+/8z5mFZpabw88w2efv4lHn90KgB//+tjXD/lRl569XX8fj//+3Jus+cbNTMh\nJmOwFJ3hJSIiIi3FbrfH2e32cXa7/RNgO/AEUA1cDuQ6HI7JDodjRSRz3NfwsqjhJSIiIlHIbrdn\n2u12Q0v/nOEDu/D592sBWLR6K0f1ah8a69Epm43bSqmuqcPr9fP98gKOO6or+Vt3Ya5fZZ+aFI/b\n4wMgMd7GQ8/P4c054Zts3WeYvTVfrCgEYPHGMgZ2zgiN2fNS2VRSRbXTg9cXYL5jF8f2bMPG4ipM\n9XWkJFhCny/3uev3A3jxv+spq6ojXAZ2SOW7jcEVdqsKq+idmxwa65SZwLbdtdS4fHj9AZZuq2BQ\nxzQ6ZyXybX4wZutuJ52yEgC46e1VLN9eidlkICPJyl6XN2x1bFm/CvuAwQC079aLHZscTcZ9Xg/j\nb32YrNz2B4wpLAjG5HbqRk11JYFAAJezFqM5fBt57dm8ltY9BgGQ3sFOxfaNTcb9Pi+Dr7ib5NZ5\nB47ZsQmAoy+7lZTcjgAE/H5MFlsYKmgQLddWxZa1ZNoHAtCqfXeq6n+/+/h9XgaMv5OErLYHjikM\nxvhcTrqcMo6cgSeGJ/lGqratI63bAABS2nWnuqhpHQGvl14X3058Zt5BYgoA2Fu0idSOvQBI73YU\n5QUrw1ECAGtXLuOowcMBsPfqy0bH2ibjXo+Hex7+B23bd2pyf05eO+56+EfNsQ3rWDz/G+6Y/Aee\nfuwB6pzOlk3+R5YvW8rwY48DoE+//qxbs7rJuMfj5m9PPkPHjp1D9406dQzXXT8FAL/fj9lsAWD9\n2jUMHBT84sXw405g4YLmb9RH3ZaGOsNLREREWtAuIBEoA54CXnE4HI6Dh4SX2xucWLGYTRHORERE\nROTw2e32K4B2wL+BN4E6IMFut09yOBxftNTPTU6Mo3JvQ0PH6/NjMBgIBAKkJMZRtbdhwrG6po6U\npDhqal10yM1gxex7SU9N5PwbXgBgW/EethXvYfRxvVsq3QPXEW+lqtbTUIc/gMEAgQAkx1uajO11\nekhJsFJT56VDVhJL/n426Uk2Lnh8XugxGck2TuidHdbVXQCJNjN76xqaB43rSLKZmzQWat0+kmwm\n1hdXc4I9k/85yujbNoWs5IZGSnaqjZcuO4rqOg+Okr2Ei8tZQ1xCUui20WTC7/djrP8ifwf7vlWA\ngQPGGIzBmMyctnz0ypN8+f7rxCUk0rnXgLDUAOCtc2KJS2iSU8Dvx1BfR3rH4LZ+jcrYP8ZgJOD3\nE5ecBsCezevY/O0cjvvjoy1fQCPRcm15XU7McYmh2wajsclr0qpD/WvS6EXZL6b+NYlPb0N8ehvK\nHEvCkntjPpcTs+3A11ZK++BqusZ7Re4fYyTg9zW5/kzWOHx1tS2bfCO1tTUkJjX83Zp+9Lfeo09/\nAAI0Xbwz7IST2FVS1OQ+e88+jD7zPLp078E7r0/nzVdf4MpJN7VwBQ1q9u4lKamhEWwyN62lX/9g\n07RxLXHx8cHYmhruuPkmJk2+Yb/HJCYmsre6utnzjZqv/pqN+xpeWuElIiIiLWY+cCHQ1uFw3Hqk\nNbug0ZaGlqj5mCciIiKxbRLBVfV/Bc5yOBwDgBOBFp0Vr66pIzmxYRLbWN/sAqiqqSM5MS40lpwY\nR2W1k8mXjuTz79fR/9ypDLnwUV6ZelnEv4RU7XSTFN/wffdgHfvGPCTHW0JjSfEWKmvcXH96T75Y\nUcigP33E8Ns/5sXrjwudD3vO0A68+93msNYAUOPykmD76Tr2urwkNhpLtJqprvPy0bIial0+Zlxx\nFCN7ZLGuqGFitaTSxVnT5vPekkJuHdMtbHXY4hNxORsm3QONJo1/aczHr05j4kPP8KcnZzFwxKn7\nbY/Yksxx8XhdjVaZBBoaEr8mpnDZN6x4/wWGXn0/1sSUlkj5gKLl2jLbfvz7DRz6NfkVMS3NZIvH\n6260evRn5PTTMSYwNCwG9rnrmjT3WlpCQiLO2prQbb8/cMi/9QMZevxIunQPNiyHHT+SzRvDOw2R\nmJRETc0vr6WkpJjrrrqcM886h1PHnA6A0djw38SamhqSU5r/7z1qZkL2LbXWloYiIiLSUhwOx2iH\nw/Gew+HwHPrRkRHa0tAUNR/zREREJLZ5HA5HDcFtpAsAHA5HEdCiE0Dzlxcw+tjgiqzBfTuyemPD\nN+7Xby6hS7ssUpPisZhNHDuwCz+s3Ex5VW1o5VdFtROzyRiar4qUBY5STh0Q3MLsmK6ZrN1WHhpz\nFFbSOTuZ1AQLFpOR4T1aszC/lIoad2jlV2WtB5PRgKn+i+Yn9snh8+WFYa9j+bZKju8W3I6xb9sU\n8nc1rJzZXFZL+/R4kuPMmE0GBnZoxYrtlfTOS+GHgj1c+epSPl+zix3lwdfmyXH9aJdev/rA5SOc\n353v2KMv65ctAGDbhjVkt+98iIgDxyQkpWCrXzGVkpaJszZ8q4nSO/Vk57rFAOzZsp6UnI6/Omb7\n4i/Z/N0cjrv+YRLSW7dUygcULddWq449KVsfXJFVsc1BUnaHFolpaSnte1C+YSkAVds3kNC6/SEi\nDhyTlNuZyi1rANiTv5SUDj1bKOv99ew7gMULvgNg/ZqVdOzc9RfFN14Jdf8t15O/PljHiiUL6dI9\nfHUA9B8wkO++/RqAVSuW07XboRu5u3eXMXni1Uy56RbOPPuc0P32Hj1Zuji4Qvj7b79m4FGDmj3f\nqNnS0FjfsfVqS0MRERGJYaGGl1Z4iYiISHT4l91u/whYDfzbbrd/CowB5h087PB8NG8FJw3twbxX\ng9tGXXP/P7lgzCAS4q3MnD2f25/4gH8/fz0Gg4GZH86npKyKaW98yYt/voTPp9+IxWzivmn/os7V\n8D2pH29dFQ4fL9rGyH45fPbAGAAmvfAdY4d3JMFmZtaXG7nr9cV8eNcoDAaY9eVGdlY4efaTdTw7\ncTj/uX80FpORB95eRl39eWRdc1LYsit8jZV95q4rZWiXdGb+ITg5ev+HaxnTpw3xVhOzlxbxt//m\n88L4AWAw8OHSIsr2uvH4/Fz/+z5cdUJHquq8/PmjdQBM/2YLU8/phdvnp87j488frQ9bHb0HH0/+\nikU8f8/1AIyddAfLv/0Ct6uOwSef2eiRhoPGAJx/3W28+Y8HMJlNmMwWzr/21rDVkdN3GKUblvPN\n07cBMPCiG9ix9Ct8bhcdhp76U2XsHzPuRgJ+P6s+fIWEtCwWzngUDJDRpQ89Ro8LWy3Rcm217j2U\n3fkrWPh88ProPXYyxcu/xud20XbwqEaPNBw0pqkWPy5xPxk9h1C+aQUrXr4LgG7n/pFdK7/B73GR\nPeiURqkZDhoD0Hn0ZeR/9AIBn5f4rLZk9h4WtjqGnXASyxcv4LZJlwNww50P8NUX/6GuzsnoM89r\nKOMAv+PG90+6+W5eePIvWMwWWmVk8Mdb7m3R3H9s5Mmj+GH+91x52cUA3P/gw/z3kznUOWs55/zf\n/2TOM195ierqKl558XlefvE5DBh4+vmXuPHmW3nogfvwer106tSZk0eNbvZ8DYHAb3JFVKC0tOn+\njvPXlPDyx2uZMMbOiAF5BwiLDllZyfy4/lgRy7VDbNev2mOzdojt+mO5doCsrOTwf7r+bQuUllaz\nbms5f31rGWcd25Fzjj/0N0ajgf5WYrd+1R6btUNs1x/LtUNs1x/Ln43sdvsIYDSQCewGvnU4HHN+\nbnz8wD/+Jie/GnMuewaAlItmRTiTw1P19mUA9L9/boQzOXwrHjiZ2StLIp3GYTu3Xza3zTnidmr/\nxR4/I3i202/92lrxwMkATJ69LsKZHL5p5/bkqv9bHek0DtsrF/Zhw87wnf/VUrq3SaDa9dtfLJRs\nM8JBurFHzAovu91+B3AWYAGeczgcr/6SeJPO8BIRERHB4w1++9Zqiex5ESIiIiLNxeFwfAV8Fek8\nRERE5Mh2ROx1U/9NnWEOh2M4wYNH2/3S5zAZdYaXiIiISGhLQ/MR8TFPRERERERERCQsjpQVXqOB\n1Xa7/UMgGfjFm86aTFrhJSIiIuJWw0tEREREREREYtCR0vDKBNoDZwKdgX8BPX7JE5hDWxr+9veh\nFBEREfm19q3wsqrhJSIiIiIiIiIx5EhpeO0G1jkcDi+wwW6319nt9kyHw1F2oICsrOQmt9PL6wCw\nxVn3G4tGsVDjgcRy7RDb9av22BXL9cdy7fLrNGxpqDO8RERERERERCR2HCkNr2+BKcA/7HZ7LpBA\nsAl2QKWl1U1uV1c76/+3br+xaJOVlRz1NR5ILNcOsV2/ao/N2iG264/l2kHNvl9LZ3iJiIiIiIiI\nSCw6ImZCHA7HHGCZ3W5fCHwETHI4HL/oMC6TKViKzvASERGRWOb2+gBtaSgiIiIiIiIiseVIWeGF\nw+G443DiTTrDS0REREQrvEREREREREQkJkXNTEio4eXTCi8RERGJXfsaXlad4SUiIiIiIiIiMSR6\nGl7a0lBEREQEd33Dy6wVXiIiIiIiIiISQ6JmJkRbGoqIiIiAR2d4iYiIiIiIiEgMipqZEG1pKCIi\nItJ4S8Oo+ZgnIiIiIiIiInJIUTMTEmp4BdTwEhERkdjl9gQbXhY1vEREREREREQkhkTNTIi5/gwv\nr1dbGoqIiEjs8vj2NbxMEc5ERERERERERCR8oqbhlRhvxmgwUFHjjnQqIiIiIhHj8fgwAGaTIdKp\niIiIiIiIiIiETdQ0vExGI2nJNnZX1kU6FREREZGI8fj8WMxGDAY1vEREREREREQkdkRNwwsgIzWO\nimoXXp+2NRQREZHY5Pb6dX6XiIiIiIiIiMScqJoNyUiJIwDsqXZFOhURERGRiPB41PASERERERER\nkdgTVbMhGalxANrWUERERGKWx+fHajZFOg0RERERERERkbCKqoZXZn3Dq6zSGeFMRERERCLD7fFp\nhZeIiIiIiIiIxBxzpBNoTlrhJSIiIrHO49OWhiIiIiKNOZc9E+kUmk3V25dFOoVmseKBkyOdQrM4\nt192pFNoFo+fYY90Cs0mWq6taef2jHQKzeKVC/tEOoVm0b1NQqRTaBbJtuifK4iqCjNT6hteVWp4\niYiISOwJBAI6w0tEREREREREYlJUrfBKT7EBWuElIiIiscnrCxAArGp4iYiIiIRkXPZWpFM4bLtn\njQPgrWWFEc7k8IwbmAfAGS8ujHAmh2/OtYOZvbIk0mkctnP7ZTN59rpIp3HY9q2IOn/Gkghncnje\nv3IQAD3u+DTCmRy+9X8ZzZ8/y490Goftz6d24+Rp8yOdxmGbO3kYXzp2RzqNwzbSnnHQ8aiaDbGY\nTaQmWrXCS0RERGKSx+sHgp+JRERERERERERiSVQ1vCB4jteeKhd+fyDSqYiIiIiElcfrA9CWhiIi\nIiIiIiISc6JuNiQzNQ6fP0DFXlekUxEREREJq30rvLSloYiIiIiIiIjEmqibDclIiQPQtoYiIiIS\nc9yhLQ2j7iOeiIiIiIiIiMhBRd1sSEZqfcOrUg0vERERiS06w0tEREREREREYlX0Nby0wktERERi\nVGhLQ0vUfcQTERERERERETmoqJsN2bfCq0wrvERERCTGuL0+ACymqPuIJyIiIiIiIiJyUFE3GxJa\n4aWGl4iIiMSY0JaGWuElIiIiIiIiIjEm6mZD4m1mEuPM2tJQREREYk6o4aUVXiIiIiIiIiISY6Jy\nNiQjNY7dlXUEAoFIpyIiIiISNvu2NLRaTBHOREREREREREQkvKKz4ZUSh9vrp7rWE+lURERERMIm\ntMLLHJUf8UREREREREREDigqZ0MyUuvP8dK2hiIiIhJD3NrSUERERERERERiVFTOhmSmxgOwu1IN\nLxEREYkd3vqGl9USlR/xREREREREREQOKCpnQzJSgiu8ytTwEhERkRiiFV4iIiIiIiIiEquicjYk\nU1saioiISAxye30AWCymCGciIiIiIiIiIhJeUdnwCp3hpRVeIiIiEkM8+7Y0NEflRzwRERERERER\nkQOKytmQxDgzNotJWxqKiIhITNnX8LKo4SUiIiIiIiIiMSYqZ0MMBgOZqXHa0lBERERiihpeIiIi\nIiIiIhKronY2JCM1DqfLS22dN9KpiIiIiISFO9Tw0hleIiIiIiIiIhJbzJFOoKVkpNSf41VVR0Jc\nUoSzEREREflpdrvdDMwAOgJW4GFgLTAT8AOrHQ7H9T/nubw6w0tERESk2fxtwtH0bt8Kl8fPDdN/\nYGtpTWhs9IBcbjm7Dx6fnze/KeCNrwowGQ08e81Q2mcm4vUHuGnGQjaVVNO7XSueuOIYPF4/m0qq\nuXHGwrDVEAgEmDP9SUq2bsJstXLWNbeQ3ia3yWPcrjpef+Q2zr72VjJz2x0w5r2np7K3shwCUFFa\nQttuvRg75Z6w1VE6dybu0m0YzBZaj7oKS6vWofGaTUvZs+AjDCYTyb1PILXviQR8XnZ++hKeylJM\n1niyTp6ApVUbSuY8i6+2EgLgqSolLqcb2WdMClsdH778d4q3bsJssXL+dbeR8ROvx/SptzB20u1k\n1b8ePxVTtGUjH770BEazmcycdoy97raw1LCvjnUfvkh18WaMZiu9z7+ehIzsJo/xuV0smf5neo/9\nI4lZeYeMcfx7BolZebQdMjpsdeyrpfizGTh3bcNotpB32tVYW7UJjVdtXELp97MxGM206juC9P4j\n8fu8FH7yAu6KXZhsCeSMugJbWhucu7ZS9Ol0DEYztvRs8k67Jqy13H9OT3rkJOPy+rnn/TXs2OMM\njY3smcV1J3XB6/PzwZJC3ltUiMVk4JGxfWiXnkB1nZcHP1rL9j1OeuQkc/dZPfD5A7i9fm5/ZxXl\nNZ6w1BAIBFj8znNUFAavkyEXTyEps+m15XXX8eWz9zHkkhtIaZ13wJjK4m0sevsZAJJa5zJk3BQM\nxvD8WzkQCFDx1Sw8ZdsxmCyknXQF5tSG9yzn5mVULfoXBqOJxJ7Hk9h7BAGflz1zX8FXWYrBFk/a\niMswp7bG56yifN6r+F21EAiQPupqzClZYaljXy1vPf83dmzJx2KxcunkO8nKzmvyGLerjqfuu5HL\nptxFm7z2ofs3O9Ywe9bz/Onh4OtQWryD1556GIPBQG6HzoybeEuz5xu1syEZqcGGV1ml8xCPFBER\nEYmoS4Eyh8NxAjAGeAb4O3CXw+EYARjtdvvZP+eJ3F4foC0NRURERA7XGYPaYrWYOG3qF0x9ZwUP\nXXxUaMxkNDD14qM477F5nPXoXCac2JWMZBuj+udiMho4/aEveOKj1dwzth8At53bh8dmr+J3j8wl\nzmJiVP/cA/3YZrd+0bd4vR6umvoMp1x0NZ++/nyT8aKCDcx84CbKdxUfMmbslHu5/N6/c+HNDxKX\nmMSYCT/rO1nNombjEgI+L23H3U/GcRdQ9tU/Q2MBv4+yr94kb+wd5P3+LqpWfomvtorKVV9itMbT\nbtz9ZJ40ntK5rwGQfcb15P3+LrLPugFTXCKZIy8JWx1rFn6D1+th0sPPMeaSa5jz2rNNxndscvDS\n/VPYs6vokDFz353JKRdcwcQHp+H1uFm/ZH7Y6ti15gf8Xg9DJj1GtzHjccyZ0WS8asdGFr10N849\nJYeMcddUsfTVByldtyhs+TdWnb8Yv89Ll/EP0GbERRTPeyM0FvD7KJn3Bh0vvItO4+6hfMVcvLVV\nlK+Yh9EaT5fxD5JzygSKP38VgNLvPqD1sefT+ZL78Hs9VG9aFrY6TundGqvZyLjnF/L3/+Zzxxn2\n0JjJaOD2M+xc8coiLntpERcMbkdaooXfH9OWGpePi57/gYc/Xsd9Z/cC4K7f9WDqh+u4/OXFfLFm\nF9ec2DlsdexYOR+f18OoP/2N/mdNYOkHrzQZ37NtI3OfupOaspJDxqz89yz6n305p9z0OASgcHX4\nvmxQV7CUgM9L67H3kDJsLBXfvhUaC/h9VH77Nlnn3EbWuXewd83/8DmrqFnzP4yWOFr//l5aHX8p\n5V/NAqDyu3dIsA+n9Xl3kjLkXDzlxQf6sS1i+YKv8Xrc3Pb4S5xz2XW8N/3pJuNbN67niTsnUbaz\nqMn9n33wT9545i94Pe7Qfe9Of5qzx1/LzY8+R8AfYPmCr5s936idDQmt8KrUOV4iIiJyRHsHuLf+\n/5sAL3CUw+H4pv6+/wCn/Jwncnv9GAzBf9CIiIiIRAO73Z4SiZ87pHsWc1cGJ++WFOxmQKf00Fj3\n3BQKdlZTXefF6wuwIL+U4fYsNpVUYa7/HJYSb8HtC66+X7m1nIwkGwBJ8WY89feHwzbHarr2HwxA\n2249KSpwNBn3eT1cdMuDZOa2+9kx/3t3JkPGnEtSaloLZ9+grmgDCR2DDcS4nK7U7dwcGnPvLsLS\nqg1GWzwGk5n4tnacO9bj2V0YirGm5eDe03Qyds/8D0gdcCrmhNSw1bFl/SrsA4K/2/bderFj0/6v\nx/hbHyYrt/0BYwrrX4/cTt2oqa4kEAjgctZiNIdvI6+KLWvJtA8EoFX77lTt2NRk3O/zMmD8nSRk\ntT1wTGEwxudy0uWUceQMPDE8yf9IzQ4HyZ36A5CQ2xVnccO15dpdiDUtG5MtAYPJTELbHtRsW4er\nrJDkzsEYW3oOrt3BayuudQd8zmoCgQB+dx0Yw7fV/KCOaXzjKANg5fZK+rRtuK67ZCWytayWGpcP\nrz/Aki3lDO6UTpc2SXy9oRSALWW1dG6dCMBNb65gw869QPDflnUeX9jqKN20ltyegwDI7Ghnz/b8\nJuN+n4fjr76b5DZtDxlz3FV3k9W5Fz6vh7qqcizxCWGqAlzFG4hr3xcAW3YXPLu2hMa8e4owt2qD\n0Rp8z7LldsdV6MBTXkRch+B7liUtG299Y8tdnI9v7x5KP/orzg0LiMvrEbY6ADatXUGvQUMB6GTv\nzdaN65uM+7weJt79GNmNVnYBZOW0ZeJdjza5b9smB916DwCg96ChrF+xuNnzjdqGV2Zqw5aGIiIi\nIkcqh8NR63A4aux2ezLwLnA30LhjVQ38rH+Fe7x+rGYTBoMaXiIiIhI1Sux2+x/C/UOT481UORu2\n8PL6gl8sCo5ZqKptGNvr9JCcYKWmzkv7rCR+eOwMnrhiMC9/tgGAgpJqHh0/iO8fPZ3MlDi+W7cr\nbHW4nDXEJSSGbhuNJvz+hoZbu+69SUnPgsDPi6mpqmDzmmUMGDGm5ZNvxO9yYrTFh24bjCYCgWBO\nfrcTo61hIttgseF3O7G27khtwXIA6oo24q2pIBAIFuqrrcK5bS3JvY8PYxX7frcNR68YTU1fjw72\nPqRmZNH4BflxjKH+9cjMacvHM57mHzdNoKaynM69BoSlBgCvy4k5ruEaMRiNBBrV0apDD+JSM2hc\nx34xhmBMfHobUtt1a/LYcNr/2jKGri2fy4mp0bVltMThdzuJa9OR6o3B1Vu1hfl49pYTCASwpmVT\n/MUsNk6/DW9tJYnte4WtjkSbmb113tBtnz8Qes9KjGs6VuvykhhnZl1RFSf2CG6z179dKq1Tgo35\n3XuDK3IGtm/FxcPa89q3W8NUBXjraps0poxGU5NrK7NTTxJaZdLk2jpAjMFgoGbPLv7zyPW4aqpI\nywvfSrWA24nR2nBd0ei68nvqMDQaM1jiCLidWDM7ULcl+J7lKtmIr6aCQMCPt7oMY1wSWWffiik5\nnaolc8JWB4DTWUN84/8m/Oh9q3OPvqRlZO33Fzxw2AiMpgM3fePiE3DW7m3udKP4DK9UrfASERGR\n3wa73d4O+AB4xuFwvG232x9vNJwMVPyc5/EHwGoxkZWV3BJpHtFisebGYrl+1R67Yrn+WK4dVH8M\nWgEMtNvt84AHHA7HV+H4odVOL0lxltBto9FAfa+EaqeH5PiGKbWkeAtVtW4mjunBvFXFPPzeSnLT\n4pl950kcf9d/eOTSQZw+9XPyi6u58uSuPHTxQG5/fUk4ysAWn4jLWRu6HQgEMB7iDJuDxaxd8BV9\njz057F+wMtriCbgbzfEFAhgMwZyM1nj87oYjTfzuOoy2BBK7DKJsdyE7/u8h4nK7YWvdMZT33vyF\nJPUcFvY69vvd+v2//PWoj/n41WlMfOgZWud1YP6ns5nz2rOcfdWNLZZ7Y2ZbPF5Xo2NkAoFDno30\na2LCwWiLD67G2qfRtWWyxeNzNb62gs3VlG5H4yrbQcE/HyQhrxvxbTphMBgonjuLzpfejy0jj91L\nP6dk3uvkjroiLHXUuLwk2hrelwwGQu9ZNXXBBtc+iTYz1U4Pc9fuomvrJF6/9hiWbalgTWFV6DGn\n9cvmmhM7ce2rS6ioDc/5XQDmuAQ8dQ2/88DPubYOEpOY3poz73uJTfM/Y+n7LzN0/E0tk/iPGKzx\n+D0HeM+qb3CFhurfs+I6HYVnTxG7PngEW3ZXrFkdMBiMGOOSiO8YbGjHdRxA1YIPwlLDPvHxidQ1\neQ869H9HDmTf7wCgzllLQmLSQR7960Rtwysl0YrZ9E32+AAAIABJREFUZKBMDS8RERE5gtnt9jbA\np8D1Dofjy/q7l9nt9hMcDsfXwGnAvJ/zXHUuD2aTgdLS6hbK9siUlZUcczU3Fsv1q/bYrB1iu/5Y\nrh1iu/4YbvQ5HQ7HH+12+9HAnXa7/RlgLlDgcDiePkTsr7Ywv5RTB+Tx8aLtHN0lg7XbG75/tKGo\nis5tkklJsOB0+RjWPYtn5qzDnpuK2xv81ntFjRuz0YjRYKB8ryu0uqKk3MngrlktlfZ+2tl7s2Hp\nAnoPHcH2/LW0ad/psGIKVi/lhPPGt2TKPykutxs1BctJ6j6YuqKNWDMbtjOzZuTiqdiJr64Go8VG\nXaGDtKPPwFVSQEL73mSdeAl1Ozfjrdodiqnduob0oeeEvY6OPfqybsn39B12Its2rCG7/aFXnBwo\nJiEpBVtccFVLSlomWx1rWjT3xlp17EnpusVk9x1OxTYHSdkdWiQmHBLyulO9aRmpPYZQW5iPLath\ne09bRh7u+mvLYLFRu8NB5pAzcRZvIqljH3JOHo+zpABP/bVljk8OreqxJLWitnBD2OpYuqWcE3tm\n8enqnfRvl8qGkoaVM5tKa+iQkUBynJk6j49BHdOY/vVm+rZNZf7G3fxljoPeeSnkpgVz/92AHC4Y\n0pbLXlpEdaOVYeGQ1bkXhWsW0n7gcZRtXk+rnENfJweK+fqlqQw89w8kZ+ViscWHtcFqy+mGc8sK\nEroeg6tkI5aMhvcsc3ou3spd+F01GMw2XMUbSD7qNNy7CrC160mr48fh3rUFb/We4HPldqdu6woS\n7MNxFW3AnJEXtjoAuvTsx6pF3zHo2JMoWL+avI6/bKXcvpW1AO06dyN/9TK69RnImiULsPcb1Nzp\nRm/Dy2gwkJ4Spy0NRURE5Eh3J9AKuNdut99HcG+GG4BpdrvdAqwD3vs5T+T2+rFZwrdPvIiIiEgY\nGAAcDsdi4Hy73Z4KnADYW/KH/nvxDk7snc0n9wSPUp38yg+cN7QDCTYTb3xVwD1vLuP9W0diMMAb\nXxWws7KO5/+7nqevHsLHd52MxWxk6rsrqPP4uHHGQl65/lg8Xj8en58bZyxsydSb6HnM8RSsXML0\n+yYDcPbE21j13VzcrjoGnXRGwwMNB4/ZZ3fxDtJa54Ql98YSux5N7dbV7Hj7QQBaj76a6vXz8Xtc\npPY9kcwRl1D0/uNAgJQ+IzAntcJgMrF7znuU//ARxrhEWp96Vej5POUlWFJbh72O3oOPJ3/FIp6/\n53oAxk66g+XffoHbVcfgk89s9EjDQWMAzr/uNt78xwOYzCZMZgvnX3tr2Opo3Xsou/NXsPD5YC69\nx06mePnX+Nwu2g4e9ZN1/FRMU5HZlj2l+zHUbFlFwRt/BiDv9GupWPs9fo+L9P4jyTnpUra88ygE\nIK3fiViS0jCYzGz/aBq7vv8QU1wieaddE4wdczXbP3oag8mMwWgi97Srw1bH52t2MbxbBm9ODJ73\ndtd7qzmjfzbxVhPvLSrkL/9ez/Q/HI0BeG/xDkqr3Xh8Aaac2o+JJ3Wm0unlnvdWYzDA3b/rQWFF\nHc+MH0iAAIsKynl27qaDJ9BM2vYfRoljGZ//PXg9D7n0RrYs/gqfu44uw0c3eqThoDEAvUb9ngVv\nPInJbMZktTF43JSw1AAQ13kQddvXsOu9hwBIO/kqajcsIOBxkdh7BKnHXUTpR38DILHX8ZgSW4HR\nzJ4fnqd68b8x2hJIO+lKAFKPvYjyeTPYu/pLjNZ40k+dGLY6AAYMG8G65Yt4/LZrAZhww90s+uoz\nXK46jjv1rNDjDvQX3Hgl7flXTuaNZ/6Cz+slp11Hjjp2ZLPna2jcYfsNCfycb3T99a1lrNtazvM3\nj4iqyZ9Y/0ZbrNYOsV2/ao/N2iG264/l2gGyspJ1ENUvE7jwrjmkp9h48A9DIp1LWOlvJXbrV+2x\nWTvEdv2xXDvEdv2x+tnIbrdPcDgcrx3Oc2Rc9tZvcvKrsd2zxgHw1rLCCGdyeMYNDK5MOOPF8DX9\nWsqcawcze2VJpNM4bOf2y2by7HWRTuOwTTu3JwDnzwjPdqEt5f0rgyteetzxaYQzOXzr/zKaP3+W\nH+k0DtufT+3GydPmRzqNwzZ38jC+dOw+9AOPcCPtGXCQDnnkN0ptQZn153jt0SovERERiQEenx+L\nOXq+5CMiIiJyuM0uERERiR1R3fDKqG947dY5XiIiIhLlAoEAHq8fizmqP96JiIiIiIiIiPykqJ4R\nyUgJNrzK1PASERGRKOepPyDdqoaXiIiIiIiIiMSgqJ4R2bel4W5taSgiIiJRzu3xAWiFl4iIiIiI\niIjEpKieEdm3wktbGoqIiEi0c6nhJSIiIiIiIiIxLKpnRNJSbBgNBsq0wktERESiXMOWhqYIZyIi\nIiIiIiIiEn7mSCfQmN1ubw0sBk5xOBwbDvf5TEYjaclWrfASERGRqKcVXiIiIiIiIiISy46YGRG7\n3W4GXgBqm/N5M1LiqKh24fX5m/NpRURERI4oHk/ws44aXiIiIiIiIiISi46kGZG/Ac8DRc35pBmp\ncQSAPdWu5nxaERERkSOKVniJiIiIiIiISCw7ImZE7Hb75cAuh8PxOWBozufOSI0D0LaGIiIiEtXc\n9Q0vqxpeIiIiIiIiIhKDjpQzvK4A/Ha7fRQwAJhlt9vPcjgcuw4UkJWV/LOeuGNeGrAVt//nx/wW\nRFMtv1Qs1w6xXb9qj12xXH8s1y6/jMe7b0tDU4QzEREREREREREJvyOi4eVwOEbs+/92u/1L4NqD\nNbsASkurf9Zz2+q/5Lx5RzmlndJ+fZJHkKys5J9df7SJ5dohtutX7bFZO8R2/bFcO6jZ90tpS0MR\nERERERERiWVH4oxIoDmfLLSlYZW2NBQREZHo5fFqS0MRERERERERiV1HxAqvxhwOx0nN+XwZKTZA\nZ3iJiIhIdHN59m1pqIaXiIiIiIiIiMSeqJ8RsZhNpCRatcJLREREopo7tKWhzvASERERERERkdgT\n9Q0vgMzUOPZUuXC5fZFORURERKRF7Gt4WS0x8fFORERERERERKSJmJgR6dUxHZ8/wGLHrkinIiIi\nItIi3Pu2NDTFxMc7EREREREREZEmYmJG5IR+ORiAr1cURToVERERkRbh8dZvaagVXiIiIiIiIiIS\ng2JiRiSzVTy9OqaRv6OSorKaSKcjIiIi0uxc+87w0govEREREREREYlBMTMjcnz/XAC+WalVXiIi\nIhJ99m1paLWYIpyJiIiIiIiIiEj4xUzDa2C3LJLiLXy3qgSvzx/pdERERESalbt+S0OrOWY+3omI\niIiIiIiIhJgjnUC4WMxGhvfJ5rNF21meX8bRPVpHOiURERGRZuOu39LQrIaXiIiISBO7Z42LdArN\nZtzAvEin0CzmXDs40ik0i3P7ZUc6hWYx7dyekU6h2bx/5aBIp9As1v9ldKRTaBZ/PrVbpFNoFnMn\nD4t0Cs1ipD0j0im0uJhpeEFwW8PPFm3n6xVFaniJiIhIVAltaaiGl4iIiEgT8Wc8HekUDptzzhQA\nLn1jRYQzOTxvXNofgNkrSyKcyeE7t182by0rjHQah23cwDzOn7Ek0mkctn2NrgUbKyKcyeEZ2rUV\nAMf97ZsIZ3L4vr3leDIueyvSaRy23bPGMXPRtkincdguP6Y9f/4sP9JpHLZDNVFjakYkLzORrnmp\nrNm8h7IKZ6TTEREREWk2+1Z4WdTwEhEREREREZEYFHMzIsf3zyEAfLuqONKpiIiIiDQbt8eHyWjA\nZIy5j3ciIiIiIiIiIrHX8Brcow1xVhPfrCzG7w9EOh0RERGRZuH2+nV+l4iIiIiIiIjErJibFbFZ\nTQzt1YbyaherN++JdDoiIiIizcLt8en8LhERERERERGJWTE5K3J8/1wAvllRFOFMRERERJqH2+tX\nw0tEREREREREYlZMzop0zE6mXesklm8so7LGHel0RERERA6b2+PDbDZFOg0RERERERERkYiIyYaX\nwWDghP65+PwBvl9VHOl0RERERA6btjQUERERERERkVgWs7MiQ3u3wWI28vWKIgKBQKTTERERETks\nbo8fixpeIiIiIiIiIhKjYnZWJDHOwtH2LHaWO1mxaXek0xERERE5LF6fzvASERERERERkdgV07Mi\nowe3x2Q0MGPOOsoqnJFOR0REROSwWHSGl4iIiIiIiIjEqJhueLVvk8wlo7qz1+nhmdmrcHt8kU5J\nRERE5FfTloYiIiIiIiIiEqtiflZkxIBcju+Xw7ade5n1qUPneYmIiMhvlrY0FBEREREREZFYFfOz\nIgaDgUtP7U6nnGS+X13CvKWFkU5JRERE5Fcxq+ElIiIiIiIiIjFKsyIEz7u4/ty+JCdYeHtuPhu2\nV0Q6JREREZFfTCu8RERERERERCRWaVakXnpKHJPO6UMgAM99uJryalekUxIRERH5RaxmU6RTEBER\nERERERGJCDW8GrG3T+OCk7pSVePmudmr8Hj9kU5JRERE5GfTloYiIiIiIiIiEqs0K/Ijo45uy9Be\nbdhUVMXLH6/B5fFFOiURERGRn0VbGoqIiIiIiIhIrNKsyI8YDAYmnNaDbm1TWewo5dE3llBW6Yx0\nWiIiIiKHZFHDS0RERERERERilGZFfoLNYuKWiwZyQv9ctu3cy4MzF7N+a3mk0xIRERE5KK3wEhER\nEREREZFYZY50Akcqi9nIhDF2OmQn8+bnG/jb28sZd0o3TjoqD4PBEOn0RERERPZjMZsinYKIiIhI\n1Hhq0on065xFndvLdU/PZUtJVWjs9MGduPOiY/D4/Mz6fB0zP1uDyWjglT+NokObFLy+AJOensvG\nogr6dc7kg/vPIr8w+GXqlz9ZxQffbgxLDYFAgK3/eYXanVsxmi10PHMicWltQuMVGxZT9M37GIxm\nMgecSNbAk/H7vGz+13O4yndiikugw5g/EJeeTU3xZrZ+8jJGs4X47I50GH1FWGrYV8eHL/+d4q2b\nMFusnH/dbWS0yW3yGLerjulTb2HspNvJym13wJiiLRv58KUnMJrNZOa0Y+x1t4W1jjnTn6Rk6ybM\nVitnXXML6T9Rx+uP3MbZ195KZn0dPxXz3tNT2VtZDgGoKC2hbbdejJ1yT9jqKP5sBs5d2zCaLeSd\ndjXWVg3XVdXGJZR+PxuD0UyrviNI7z8Sv89L4Scv4K7YhcmWQM6oK7CltcG5aytFn07HYDRjS88m\n77RrwlJD41pee+5xthfkY7FauXLK3bTOyWvyGFddHX+9dzJX3Xgv2XntQ/dvWr+ad2Y+y51/eb7J\n4998+Uly2nZg5GnnhqWGfW4+pStdsxJx+/w89mk+RZV1obFjO6czYVh7vH4/n6zeyb9X7cRsNHDX\nmO7ktoqjxuXliS82UVRZR9esRB4/rzfby4O7nn24vJgvN5SFrY6/TTia3u1b4fL4uWH6D2wtrQmN\njR6Qyy1n98Hj8/PmNwW88VUBJqOBZ68ZSvvMRLz+ADfNWMimkmp6t2vFE1ccg8frZ1NJNTfOWBi2\nGgKBAJ/OfJpd2wowW6ycdtWfSGud0+QxHlcdbz92B6dffQsZOW0PGFNbVcEn0/+Bq2Yvfr+f3028\njVY/eq6WrmXxO89RUbgZo9nKkIunkJSZ3eQxXncdXz57H0MuuYGU1nkHjKks3sait58BIKl1LkPG\nTcFgbN4v7uprwAdhMBgYOTCPW8cNJCnezD8/38Crn6zH49W5XiIiInLk0ZaGIiIiIs3jrGGdsVlM\njLzlXe577Xsev+r40JjJaOCxq47n9Ls/5NQ7PuAPY3qTmRLPmGM6YjIaOenW93j07YU8OGE4AAO7\ntuap2Us57a7ZnHbX7LA1uwAqHIsIeD30uuIh2p50Mds/ey00FvD72Pb5LOyX3ov9svspXfoFnpoq\nSpd+gckaR68rH6bD6CvY+t/pAGyZ8xLtx1xBjwkPYLYlsHvVt2GrY83Cb/B6PUx6+DnGXHINc157\ntsn4jk0OXrp/Cnt2FR0yZu67MznlgiuY+OA0vB4365fMD1sd6xd9i9fr4aqpz3DKRVfz6etNGyVF\nBRuY+cBNlO8qPmTM2Cn3cvm9f+fCmx8kLjGJMROuD1sd1fmL8fu8dBn/AG1GXETxvDdCYwG/j5J5\nb9DxwrvoNO4eylfMxVtbRfmKeRit8XQZ/yA5p0yg+PNXASj97gNaH3s+nS+5D7/XQ/WmZWGrA2DJ\n/K/wetzc+8Qr/H7CJN565ckm45vz1/HoHRMpLSlqcv8n77/OjGmP4vV4QvdVV1bwxP03snzhN2HJ\nvbETumZgNRm47q0VvPj1Fv54YqfQmMkAfxzZmRvfXcXk/1vFWf1yaBVv4ax+2dS6fUx8cwVPzivg\n5lO6AGBvk8Tbi3dwwzuruOGdVWFtdp0xqC1Wi4nTpn7B1HdW8NDFRzXUYTQw9eKjOO+xeZz16Fwm\nnNiVjGQbo/rnYjIaOP2hL3jio9XcM7YfALed24fHZq/id4/MJc5iYlT/3AP92Ga3YfF3+DweLrv/\nKUZccCVz//lCk/HizRt446GbqWj0t36gmHlvvUyfY0/mknue4ISxl7O7eHvY6gDYsXI+Pq+HUX/6\nG/3PmsDSD15pMr5n20bmPnUnNWUlh4xZ+e9Z9D/7ck656XEIQOHq5m9CalbkZ+jerhX3XX4MHbKT\n+XZVMY+8sbTJt3pEREREjgTa0lBERESkeQzvlcvnS7YBsMixk6O6Naxe6dEunY1FFVQ73Xh9fr5f\nW8xxfXLJL6zAbAp+HktNsOKu/8L0wK6tGXNMRz77y/k8N+VkEmzh23Cpevt6UrsMACAprxs1xQWh\nMWdpIXHpOZhsCRhNZpLa96R661rqynaQ2nUgAHEZudSVBSf6PdW7ScrrFnyutnaqt68PWx1b1q/C\nPmAwAO279WLHJkeTcZ/Xw/hbHyYrt/0BYwoLgjG5nbpRU11JIBDA5azFaA7f67HNsZqu/YM5te3W\nk6KC/eu46JYHycxt97Nj/vfuTIaMOZek1LQWzr5BzQ4HyZ36A5CQ2xVn8ebQmGt3Ida0bEy2BAwm\nMwlte1CzbR2uskKSOwdjbOk5uHYHr6u41h3wOasJBAL43XVgDO+uFflrV9B30DAAuvTow+b8dU3G\nfV4vU+55nJy2HZrc3zqnHVPueazJfXV1tZx7yTUMH3layyb9E/q1TeGHLcFVpGtLqumRnRwa65CR\nwI5yJ7VuHz5/gJWFlQxol0rHjAQWbN4DwPZyJ+3TEwDokZ3E8M7pTLuwH7ef2o04S/j+nTmkexZz\nVwavjSUFuxnQKT001j03hYKd1VTXefH6AizIL2W4PYtNJVWYjcEd2VLiLbh9fgBWbi0nI8kGQFK8\nGU/9/eGwY8NqOvc7BoC8rj0pKdjQZNzn9TL2pgfIaPSetV/M5vzg/flrqNpTxlt/uZ218+fRvmf/\nMFURVLppLbk9BwGQ2dHOnu35Tcb9Pg/HX303yW3aHjLmuKvuJqtzL3xeD3VV5VjiE5o9X82K/Ezp\nKXHceclRHNc3h60l1UyduZhZnzrY6/QcOlhEREQkDLTCS0RERKKd3W632u32+Jb+OckJViprXaHb\nXp+ffSdcpCRYqWo0Vu10k5Joo8bpoUN2CiteHM+0P57Ec/9aAcAiRwl3Tf+OU+94n80lldxzyZCW\nTj/E56rFFNcwoWgwmggEgpO+PnctJlvDr9JkicPnriUhuxMV+UsA2LtjA57qPQQCAWxpbajeFmwG\nVOQvxu+pI1xczhriEpJCt40mE35/w+R1B3sfUjOygMABYwzGYExmTls+nvE0/7hpAjWV5XTuNSAs\nNTTklBi6bTQ2raNd996kpGc1LuOgMTVVFWxes4wBI8a0fPKN+F1OjI2uHYPR2HBduZyYbA3XnNES\nh9/tJK5NR6o3Bldv1Rbm49lbTiAQwJqWTfEXs9g4/Ta8tZUktu8V1lqctTUkNLpOTCZzk9eka8++\npGe2JhAINIk7eviJmExNm3NZbXLp3L0XTR8ZHglWE3td3tBtnz/AvkN5Eq1mahqN1bp9JFpNbNi1\nl+Fdgg2l3jnJZCVZAVhTXM2zX21m8v+tpKiyjiuHN232taTkeDNVjebbG7/3JsdbqKptGNvr9JCc\nYKWmzkv7rCR+eOwMnrhiMC9/FmwuFZRU8+j4QXz/6OlkpsTx3bpdYavD5azFltDo78BkItDoumrb\nrRfJ6ZlNrqv9YoxG/H4flaU7iU9MZtwdj5GcnsX8j98OTxH1vHW1TRpTRmPTWjI79SShVSaN37gO\nFGMwGKjZs4v/PHI9rpoq0vI6N3u+mhX5BawWE1ee0ZNbLhpAdkYC/1tWyJ0vzud/ywrx+yPxViYi\nIiLSQA0vERERiTZ2u7273W5/z263v2m324cCq4E1drv9wpb8udW1bpLjraHbRoOBffOSVbVukhMa\nxpLjrVTWuJh8zgA+X7KV/te+zpDJb/LKzaOwmI18PL+AFQWlAPxr/ib6dc5qydSbMNkS8LkaNaYC\nfgyG4GdGkzUBn8sZGvK5nZhsiWT2H4nJGs+61+6n3LGYhJzOGAwGOv3uOoq/nY3jjamYE1thTkgJ\nWx22+ERcztpGZfgxHuLclwPFfPzqNCY+9Ax/enIWA0ecut/2iC1pv5wCgV9eR6OYtQu+ou+xJ2PY\n1xEIE6MtPrgaqyGphuvKFt/kuvK7nRhtCaT1HYHRGkfBPx+kKn8x8W06YTAYKJ47i86X3k+3q/5K\nq97HUzLv9bDWEp+QSJ2z4Ywof+DQ19aRqNbtI8HasFrRaGhoP9S4vU1WliZYTVS7vHyyeie1bh/P\nXNiP47pm4Ni5F4Bv8neTvyv4O/k6v4xuWQ0N15ZW7fSSFGdpqMPY8N5b7fSQHN9QR1K8hapaNxPH\n9GDeqmKG3D6HE+/5D89eOxSr2cgjlw7i9KmfM/zOT3jnu808dPHAsNVhi0/AXdfwdxAI+A95VtVP\nxRiNJuKTUuh61FAAuh01NLTyK1zMcQl4muQVOGQtB4tJTG/Nmfe9RNfjTmPp+y83e76/vb/eI0Cv\njuk8cOVgLhjZFa8/wKxPHUydtZhNRZWRTk1ERERimNUS3u0/RERERMLgZeAF4H3g38BIoC9wY0v+\n0Pnrihl9dHBVw2B7Nqu3Npxhs377HrrktCI10YrFbOTY3rn8sK6Y8r0uqmqCK78q9rowm4yYjAY+\nnnoOR3VtDcDI/u1YtjF8qwyS2tqp3LgUCK7Wim/dsH1WfFYerj0leOtq8Pu8VG9bT1Lb7tQUbSSl\nU196TniA9F5DsbUK5l6Rv5TO507Bfum9eGurSO3UL2x1dOzRl/XLFgCwbcMastsfelXAgWISklKw\n1a96S0nLxFm7t4Wy3l87e2/yl/8AwPb8tbRp3+kQEQePKVi9lK712zaGU0Jed6oLlgPB1Vq2rIYt\nGG0ZebgrduKrv65qdzhIyOuGs3gTSR370PmS+0jtMQRr/XVljk/GaA2uFrMktcJXV7v/D2xB3Xr2\nY8Xi7wHYuH4V7Tp0+UXxgYis59rfysIqhnUKbmvZOyeZTWUNv8etu2tp2yqOJJsJs9FA/7aprCmq\nomd2Mku2VfDH/1vJ/zaUUVQZbGL+fWwf7G2Cq94GdWgVaoSFw8L80tBZW0d3yWDt9orQ2IaiKjq3\nSSYlwYLFZGRY9ywW5ZdRWeMOrfyqqHFjNhoxGgyU73Wxty64sq2k3Elqoy8qtLS23XuzaXnwfKrC\njWvJanvov/UDxbSz9wndv239KrLahm/FHUBW514UrV0MQNnm9bTKOfTPP1DM1y9Npbo0uGWlxRZ/\nyMbZrxG+TWqjjNlkZMyQ9gzp1YZ3/7eRBWt28vCsJQzslsnpwzrQJTc10imKiIhIjLGY9F0mERER\niTpmh8Pxhd1uNwCPOByOQgC73d6iZ0x89P0mThrQjnl/HQvANf/4ggtGdCfBZmHmZ2u4/ZVv+PfU\nczAYDMz8bA0l5bVM+3AZL954Cp8/dj4Wk5H7Zn5PndvH5Gfn8Y+JJ+L2+thZXsv10+a1ZOpNpPUY\nTNXmlax79V4AOp11HbtXf4vf4yJr4Mm0O3UCG/75EIEAZA0ciTU5DaPJzKYPnuT/27vv8Diqc4/j\n363qkm1J7r0dFzAQMNWA6S1ASMjlkksIhI4hQOg1EEIgIRAIJQUChBASIAkhpoZQTe8uYI6Nu3GT\nZVld2nr/mJG0kiVXSavV/D7Ps8/ulDN7Xu2s9GrfPWdWvfVPgtl5jDr2PACy+w3C/vmn+MNZFIyY\nTNHY7psKcPKe+7Nw9of89roZAJx4/lV89tZ/iTQ2sOch30zZ07fZNgDfOe8KHv/1TQSCAQLBEN85\n5/Jui2Pi1P1ZPOdj/njDhQAcf+4VzH37FSKNDex+8DHthdFumyblq1fSt/+gbul7qsLxU6ldOpfF\nj90IwJCjz2HjF++QiDbSb5eDGHTwKSx98lZIQt8p0wnl98UXCLLimXtY986/CGTnMeSos522R57F\nimd+gy8QxOcPMPios7o1lt33nc68zz7g5suc5z3rkut59/WXaGxsYPoRxzfv19EoOh+bru/e8XaO\nNxeWM3VEX+4/2SlE3/riQg6dUEp2yM+zc9dyz+uLufPEnfH54Nm5ayivjRKNJ7lpvwmcutdwqhtj\n3PaiMxXg7S9/xY8PGUM0kWRDbYRf/qf7RhQ9+9FKpk8eyPPXHQrAhQ++z7f3HkFuVoDH3ljMdY9/\nyj8uPwifDx57YzFrKxv47Ytf8puz9mLmNYcQCvq5+anZNETjXPzQBzw4Yz+isQTReIKLH/qg2+IY\nv8c0lsz7hEdvugiAY86+nM/feZVoYwO7HnR0836p51V7bQAO/t45PP/gHXz6ykyycvI4bsY13RYH\nwNBd9mGN/ZSX73T6s9cpF7P0ozeIRxoYs+8RKXv6NtsGYNJh3+W9x+4iEAwSCGex58k/6vT++trO\nP5ohkmVl1enuQyt2eQVPvb6IxauqAJgwvA8U7IMMAAAgAElEQVTH7DOSSSP7dvqw4tLSAnpa/N3F\ny7GDt+NX7N6MHbwdv5djBygtLUjH/wkZ69hLn0nees7eDOjb+Rd97en0XvFu/Irdm7GDt+P3cuzg\n7fi9mhsZY/4CBHC+tD0KeBGoBHa31m7VtIY5x/wmIz/8SlX/nPOh4CmPzU5zT3bMY6fsAsDTc9ak\nuSc77oQpA/nrp1+nuxs77OTdhvCdhz5Odzd22D9+uDsA7321cQt79mx7j+0DwLRfzUpzT3bcW5ft\nT/Gpf013N3ZY+aMn88iHy9PdjR122tTh3NiNxcuucuPh42AzdWWN8OokZnhfrv3+7ny5fCPPv7uU\nz5dW8OXyzxg5sIBj9hnBbuNL8XfzfLoiIiLiLRrhJSIiIr3QD4CjgQVADXAJUAf8MJ2dEhERkZ5H\nBa9O5PP5mDiiLxNH9GXJ6iqef28Zn9gy7nt6HiVF2ew/ZRDTpgymb0FWursqIiIivZCu4SUiIiK9\njbU2Bvw7ZdWl6eqLiIiI9GwqeHWRUYMKmXHCzqwur+XF95fz/vy1PD1rCf96awlTRhdzwK6DmTKm\nmEAXXJhNREREvCkUVF4hIiIiIiIiIt6kglcXG1Scx+lHT+R/DxnH+1+s5c3Zq5i9qJzZi8opyg+z\n306D2HNif4b1z+/0a32JiIiIt6jgJSIiIiIiIiJepYJXN8nJCjJ9tyFM320Iy9dWM2v2at79fA3P\nv7eM599bxoC+OUyd2J+pEwYwtDRPxS8RERHZJsGAX9cLFRERERERERHPUsErDYYPKOD/Di/guweN\nYc6icj78ch2zF63n2XeW8ew7yxjYL5epE/qz67gSRgws0IdXIiIiHmCM2Qu4zVp7kDFmDPAIkADm\nWWtnbKl9OKTRXSIiIiIiIiLiXSp4pVE4FGCPCf3ZY0J/GiNx5iwu58P5a5mzqJyZ7yxl5jtLKcoL\ns/PoYqaMKWbyqH7kZOklExER6W2MMZcD3wdq3FV3AtdYa2cZY35rjDneWvvM5o4RDgW6upsiIiIi\nIiIiIj2Wqic9RFY4wNQJ/Zk6oT8NkRjzFm9g9qL1zF1UzltzV/PW3NUE/D7GD+vDXjsNYnhpLsP7\nF+D3a/SXiIhIL/AVcALwZ3d5d2vtLPfxC8BhwOYLXrp+l4iIiIiIiIh4mApePVB2ONg88iuRTLJ0\ndTVzFq1n9qJy5i+rYP6yCgBys4KY4X2YNLIfE0b0ZXBxrq79JSIikoGstU8bY0akrEr9g14NFG3p\nGBrhJSIiIiIiIiJepoJXD+f3+Rg9uJDRgwv51v6jqaxpZGVFAx/MXcX8ZRV8unA9ny5cD0BhXphx\nQ4oYN7SIsUP7MHxAPsGAvu0tIiKSgRIpjwuAjVtqEA4FKC0t6Loe9XBejh28Hb9i9y4vx+/l2EHx\ni4iIiEj7VPDKMEX5WYwdVcLkYc4Xvcs21jeP+rLLK/h4QRkfLygDnKmNRg8uZOzQIkYPLmLUoEKK\n8sLp7L6IiIhsnU+MMQdYa98EjgJe3VKDcNBPWVl11/esByotLfBs7ODt+BW7N2MHb8fv5djB2/Gr\n0CciIiKyeSp4ZbjSPjmU9snhgF0Gk0wmKa9sYOHXlXy1spKFKzdil2/ky+UtXwovLsxi1KBCRg0u\nZPSgQoYPKCAnS6eBiIhID3MZ8IAxJgTMB/6+pQaa0lBEREREREREvKxHVDqMMUHgIWAkEAZusdbO\nTGunMpDP56OkTw4lfXLYZ/JAAOoaoixaVcWS1VUsWVXF4tVVfGTL+MiWNbfr3zeHEQMKGD4g370v\noFAjwURERLqVtXYZsK/7eCEwfVvaq+AlIiIiIiIiIl7WIwpewCnAemvtqcaYvsBngApenSA3O8TO\no4vZeXQxgDMKrKqBJaurWbyqkmVrqlm+toYPv1zHh1+ua27XtyCLIaV5DCvNZ2hpPkP75zOoOFfX\nBBMREemh9pw0IN1dEBERERERERFJm55S8HoSeMp97AeiaexLr+bz+SgpyqGkKIepE/oDNE+FuGxt\nNcvW1rB8bTUr1tUwb/EG5i3e0Nw24PcxsDiXISV5DC7OY3CJc+vfN0eFMBERkTQ7at9Rnr2miYiI\niIiIiIhIjyh4WWvrAIwxBTiFr2vT2yNvSZ0KcXfTv3l9TX2Ur8tqWFlWy4p1Nc2Pvy6rbdU+4Pcx\noF8ug4qd28B+uQwqzmNgv1xdH0xERERERERERERERLpcj6lGGGOGAf8E7rXWPrGl/UtLC7q+Uz1Y\nd8RfCowa3q/VukQiyfrKepavqWbFWue23L1ftb52k2P0K8xiSGkBg0ud0WCDSvIZXJrHoOK87b7W\niF5778av2L3Ly/F7OXYREREREREREZGt1SMKXsaYAcBLwAxr7Wtb08bLU/aUlhakNX4fMKIklxEl\nuTDZuV5IMplkY02ENRvqWFNey+oNdawpr2PNhjrmLVrP3EXrNzlGv8Is+vfNpbRPDv375tC/T07z\n445GhqU79nTzcvyK3Zuxg7fj93LsoGKfiIiIiIiIiIhsvR5R8AKuBvoA1xtjbgCSwFHW2sb0dku2\nls/no29BFn0Lspg4om+rbZFonLKN9azZUM+6ijrWVtSxdkM9ayvqmL+sgvnLKjY5Xn5OiJKibEr7\n5FDSJ5vSIufe4INYglBQ1wwTERERERERERERERFHjyh4WWsvBi5Odz+ka4RDAYaU5jOkNH+TbY3R\nOOs31rNuYz1lFc79uo31lG1sYGVZDUvXtD+yoSg/TElhNsVFzq3pcb/CbIoLs3XtMBERERERERER\nERERD1FVQNIqazPFsEQySWVNhLKN9ayvrGf9xgaqG2N8vbaa8qoGlq6pZtGqqnaPm5MVaC5+9XNH\nnvUrzG51n7Wd1xATEREREREREREREZGexZdMJtPdh+2R9Po1Tbwaf2rsiUSSjTWNlFc1sL6ygQ1V\nDWyocpY3VDVQXtVIfWOsw2PlZQfpU5BF3/ys5ukYU5eL8rMoyA3h9/m6K7wt0muv2L3Iy/F7OXaA\n0tKCnvMLODN4Nj/Se8W78St2b8YO3o7fy7GDt+NXbrRDMvLDLxEREWlXhzmRRnhJxvL7ffQrdKYx\nHDe0/X3qGmJUVDdQUd3IhupGpyhW3UiFe9tQ1cjXZbUdPkfA76MwL0yf/Cz65Dv3RU33eS3Lhblh\n/H797yEiIiIiIiLS0+Sc8GC6u7DD6p8+E4BHPlye5p7smNOmDgdg1CXPpbknO27Jr4/h9tcXp7sb\nO+zy6aMz/ryClnPri1Udf86XCSYNzgNgz5+/ntZ+dIYPrpnOmU/MS3c3dtiDJ+3Ua97r0341K93d\n2GFvXbb/Zrer4CW9Wm52kNzs9qdMbNIQiVFR3cjG6kYqapxCWGVNhI01jWx071esq2bJ6o6/EObz\nQUFumKK8llthfpiivCwK80IU5YYpzHNueTk9a9SYiIiIiIiIiIiIiEimU8FLPC87HGRQcZBBxXkd\n7pNMJqmpjzqFsNqWgpizHKGyppGqWud6YyvW1Wz2+fw+HwVuEawgL0xhbogCtyBWkBuiMDfsLLvr\ns8K61piIiIiIiIiIiIiIyOao4CWyFXw+HwVuIWooHY8WA2iMxKmsi1BVE6Gy1imEVdZGqKqLUlUb\nab6t21jP8i0UxwDCQb/73CGK++QQDvgpyA25tzAFOc59fm6I/JwQudlBjSATEREREREREREREU9R\nwUukk2WFA/QP59C/T84W941E41TVRahuKoa5j6vrIlTVRqmuj1Dt3q8sq2Xpmi1fnNnng7xspyCW\nn+Pc8tz7gpTH+Tkh8rKDzduDAX9nhC8iIiIiIiIiIiIi0u1U8BJJo3AoQElRDiVFWy6OJZNJCopy\nWbJ8A9V1UWrqm4pjTkGstj7qrndu1XVR1pTX0fGVx1rLCgfIz24qkAXJy3YKYXnZTY+D5Gc7I8ic\n9c62cEhTLoqIiIiIiIiIiIhIeqngJZIhfD4fOVlBSvvkULoVo8cAEskkdQ0xpxjmFsJq61uKYrUN\nseZ1tfVRahqirNlQR2M0vtX9Cgb85GUHnUJYU0EsO0hudojcLOdxTtO2LGe/3OwguVkhsrMCmn5R\nRERERERERERERHaYCl4ivZjf52uevnDANrSLxhLUNUSpcYtlTcWwuoYYtQ1Ooay2vvVydV2UtRvq\nSSS3dkwZ+ICcVkWwYPNyTpaz3LRuYP8Coo3R5vU57i0U1FSMIiIiIiIiIiIiIl6ngpeIbCIU9FOU\nn0VRftY2tUsmkzRE4tS6xbG6hhh1jbHWyw0xahuj1Lvb6hqddWsr6mmMbP3IsibBgJ/crEBzAazl\nFiAnHCS76XFWkJyw8zg77O4TDpCdFSQ7HNA1zEREREREREREREQymApeItJpmqZdzMkKQtG2t48n\nEtQ3xqlrjDUXxOrdglggFKCsvLZlnXvv3OLUN8aoqG4kEktsV9/DQT/ZKQWwHLcolrrOuXXwuLmd\nUzzzaapGERERERERERERkW6jgpeI9BgBv5/8HD/5OaFNtpWWFlBWVr3FY8TiCRoiTtGsoakgFok7\njyPx5iJZQ2OchkjLuoZIjDp3XWVtZLtGm7XE4WsuhGWFg2SFAilFMmdddjhAdihAVti5OcvB5sdZ\nIfcWDtBnO4t4IiIiIiIiIiIiIl6hgpeI9CrBQMdFs22RSCRpjMZbCmaRGA2ReHOhrKG9ddF4q/WN\nkThVtREaIjFi8a2/tll7An5fcwEsOxwgHEopmIVaF8iyQn6yQgHCTUU193HLfu52d9nv12g0ERER\nERERERERyWwqeImItMPvT5mesRM0jTxriMRojCace7co1hB17yNxGqOp65zCWQKoqY0071dTH6Wx\nsmG7p29sKxT0NxfCwk2FsKDfKZIFmwpjKdvcx07RzN+8TzjkJxx017n7hoN+QkFN8SgiIiIiIiIi\nIiJdSwUvEZFusCMjzzqazrFpFFok2lI0i0QTNERjNEYSRKJuAS3lFokkWi83P3b2r6mP0ljVQCTa\nudMoNhXDmoploaC/pbDWajlAKOQn7C6Hg3769c0j0hAhFGwqqqW0CfoJuccNBQMEAz4V10RERERE\nRERERDxIBS8RkQyVOgqtqJOPnUwmicQSbpEsTmPMKYhFUopjjdE4kZT1zfs3r09p07xfgtr6aPNy\ncsdmetyED9yCWeuCWFOhLBR0R52F/IQC7n7N21K2u7em7aGAn7B7HwoFWi0Hg378KrKJiIiIiIiI\niIiklQpeIiKyCZ/P13zNL3K75jmSySSxeJJIzCmERWJxotEEje590/qsnDDlFbVEogmiMafgFk1t\n4xbXorGWQls07hTUGiJxquqiRGPxHb6O2uYEA77mglko0FIwayq6BYP+Nus33a+95ZLyemprGlqt\nDwbaPA74dR02ERERERERERHxPBW8REQkLXw+H6GgUyjKy+54v46mdNxWiUTSKY41FcncUWbReMIt\nsCWIxloKZ9H2lpv3jTcvx2KJTfava4g6+8YSXVpoaxLw+1oV1ZwCnDPFY8u6lvvmolnTuqCvuXjW\ndJxgwFkfCrRu23IMX/P+TW0DAR8Bv6aVFBERERERERGR7qeCl4iIeILf7yMrHCArHOjW500kkynF\nsERLIcxdjsTixOKJVvs0LYeyQlRW1bcqrqUeo3ldvHXbWDxJfWOkeXs80fVFtyY+SCmCOYW4oL+p\nMOZzC2PutpSCWettzpSRZ317l27rt4iIiIiIiIiIZDYVvERERLqQP3V6yG3UaaPb3KJbrE2BLBZv\nWd9UHGt63LQ9dZ3TPpnyuM2+TevjCaKxluVILE5dY2ybC3AqeImIiIiIiIiIyNZSwUtERKSX25Gi\nW1dIJJPE3QJZNJ4gHk+2KrjF4kmSye4blSYiIiIiIiIiIplPBS8RERHpVn6fD797/bacdHdGRERE\nRERERER6BRW8RERERERERERE2rj7nP2YMrIfDdE45903i6VrW6YbP3qP4Vz9P7sSjSV59NUFPPJf\nS8Dv48EfHciI/gXEEgnOv38WX62qoqQwm/vPn0ZRXhYBv48z7n6dZetquiWGZDLJS4/8hnXLFxMM\nhTnqzB/Tt/+gVvtEGxv42y+u4uizLqN40NAO29RVbeT5P/6axtoaEokEx557BX3aHKsr3XziTkwc\nXEhjLM5VT8xhRXl987ZDJvfnwsPGEU0k+Pv7K3ni/RWEAj5+efIuDC/Opbo+yg3/+Jzl5XXNba49\nfiKL19Xw13dXdFsMyWSStx+/lw0rlxAIhdn/+xdRWNr6ZxiLNPDCXddywA8uoWjA0C22+eqD1/ji\ntZkcd+Wd3RpHbzmvkskkv7/rVpYuWkAoHGbGZTcwcPDQVvs0NtRz4+UzuOCKnzBk2Ijm9Qu+mMuf\nH7iHm3/9h1b7v/nfF3j+X09w272PdEcIza48chzj+ucTiSX42fOWVRsbmrdNG1vMGdNGEIsneXbO\nGp6ZvZqg38cN35zAkD7Z1DTG+eVLC/g6pc0Rk/rz3T2GcOajn3ZbDMlkkq+e/QO1a5bhD4YYd/x5\n5PQb2GqfeKSReY/+lHHfmkFuyeAO29RvWMOCf94LPh95A4Yz9ptndWscveG93uTSQ8cytjSPSDzB\nL15ayKrKlvNkv9H9+ME+w4klEjw/by3Pzl1L0O/jmiPHM7hPNrWNMe747yJWVTYwtjSPX357Misq\nnN/f//psNa8tWN+pffV36tFEREREREREREQy3HF7jSAr5Oegq2dyw58/5Jen79W8LeD38YvT9+Lo\nn7zA4dc/yxmHG0oKszly92EE/D4OvmYmtz75KT/9v6kA3HLqnvz1ja844vrnuOnxjzFD+3RbHAs+\nept4NMqpP7mbA//nh7zyl9+12r56yQIe+9mlbFy3eottXv3rA+y03yH833V3cMCJp1G+uvsKRYfv\nPIBw0M+Jv3mH25+1XHf8pOZtAb+Pa4+fxCm/fZ+T732Pk/cZTr+8MP+793BqG2N85+53uOnpL/jp\ndyYD0DcvxENnTeWQyQO6rf9Nln32DolYlOOuvJOpJ5zG+0890Gr7+mULefZXV1C9fs1WtVm//CsW\nvP2fbut/k95yXgG8/9ZrRCMRbrv3Eb5/5oU8fH/rYsIi+wXXXXwWa1evbLX+6b/9ifvvuJloNNJq\n/eKFX/LKC890eb/bmj6+hFDAz5mPfsp9ry/mkkPGNm8L+HxcfOgYLnh8Nuf+5TO+tdsg+uSG+NZu\ng6iLxDnj0U+54+WFXHHE+OY24wfkc+wu3Vd4bFI+/wOSsRi7nvVzRh76fyx58ZFW26tXLWLOQ9fT\nULF2i20Wv/gIIw/9HruccTPJZILy+R90Wxy95b0OcMDYYsIBH+f9dTa/f3MpF0wf1bwt4IMLDhrN\nxU/N5cIn5nLclEH0yQlx3JSB1EXinPv4bO56dTGXHjoGADMgn799tJKLnpzLRU/O7fRiF6jgJSIi\nIiIiIiIiGcAY4+uu59p34kBe/sT5gPvDhWV8Y0xp87YJQ/vw1eoqquujxOJJ3pm/lmmTBrJwVSXB\ngPNRW1FumEgsDsA+EwcwpDiPZ39yFCcdMIY3563e9Am7yMoF8xg9xSm8DRk7kTWLF7TaHo/FOPGS\nmygePLzjNksWOusXfk7VhvX89bYr+eLdVxk+cZduigKmjurHm1+WAfDZ8o3sPKyoedvYAfksLaul\npjFGLJHkw8Ub2GtsP8YNzOeN+U6bJWW1jB2QD0BuOMhdLy7g6Y9WbvpEXWzNV18wdPIeAPQfNYGy\nZZu+HoeddwNFA4dtsU1DTRUfP/Mo+5x0bjf1vkVvOa8A5s/9jG/suS8A4yftzCL7Ravt0ViUq26+\nkyHDR7ZaP2jIMK786R2t1lVXVfL4H+/njAsu79I+t2eXYUW8t3gDAJ+vqmbCoILmbSNLclmxoZ7a\nSJx4IslnKyr5xvA+jCrJ451F5QAs31DPyJJcAIpygpx74CjufHlht8dRtXw+fcftCkDhsPFUr1rU\nansyFmPS964kp2TIZtosBqBm1SKKRjrF8X7jvkHF4jndEQLQe97rAFOGFvL+0goAvlhTzYSBLefW\niOJcVlbUU+eeW3O+rmTXYUWMLM7lvSXO+biiop7h/Zxza8LAfPYd3Y97TprClYePIzvU+eUpFbxE\nRERERERERKRHMsaMMca8aIxZBkSMMe8ZYx43xgzcYuMdUJAborKuZeRGLJHA55bbCnPDVKVsq66P\nUpgXprYhyogBBcy+90TuOW8a9z/3OQAjSvPZUNPIN296gZXra7js2933gX5jfR1ZubnNy/5AgGQi\n0bw8dNwkCvqVkEwmO27j95NIxKksW0tOXgEnX/ULCvqV8u7Mv3VPEEB+dpDq+mjzcjyRbH498rOD\nVDe0bKuNxMjPCvL5yioOntQfgF1H9KF/UTYAX1fUM2dFJT5ft9VPm0Ub6gjn5DUvt309BoyZSF7f\nEkh5PdprE49FmfXnu9jru2cTDGe32r879JbzCqCurpbcvPyWfgUCJFJimTB5F4pL+0ObH/He+x9M\nIBBoXk4kEtx3+085/fwfk52T0yr27pAXDlDTEGtejieSNJ3heVkBahpbttVF4uSFAyxYU8O0scUA\n7DS4kNL8MD7g2qMNd/33K+qjCbr7XRJvrCeY1XKe+Pytz63C4YaswuJW5/ymbfwkE/FWr1kgnE28\noWVK067WW97rALnh1udPq3MrHKS2vXNrXQ37jukHwORBBZTmhwH4fHU1972xhAufmMOqygZ+uG/L\nFKGdRQUvERERERERERHpqe4DfmStHQHsD7wG3AH8sSuftLouSkFOqHnZ7/M1f85YVRdpta0gJ0Rl\nbYQLj92Zlz9dyS4X/J29f/xPHrxoOuGgn/LqRp7/cDkAz3+4nN3GlHRl11vJyskl0tByratkMoHP\nv/mPA9tr4/cHyMkvZOw39gZg3Df2bh6h0x1qGmLkZQebl30pr0dNQ4z87JbXIy8rSFV9jKc+WEFN\nY4wnLtibw3YawLwVld3W346EsnOJpnzonkwkt/h6tNdmw8olVK1bzduP38trD97GxjUreO/JP2zm\nKJ2rt5xXALm5edTXp/x8k0n8W4ilPYsWzGf11yv43V0/546br2blsiU8dN8dW27YSWojcXKzWt4j\nfl9Lvae2MU5eyra8cIDqxhgz56ymLhLn96fsygHji5m/poYJgwoY2jeHq44cz8+On8jIkjwuPmRM\nt8URyMohFmm5PhTJLb9H2m8TgJSidjzSQDA7r53WXaO3vNfBKWLlhjs4tyKxVuddrntuPT9vLXWR\nOPeeNIVpY4uxa53rVs5aWM7CdbUAvLlwPeNKO/81UcFLRERERERERER6qiJr7QIAa+17wH7W2o+B\nvl35pO9+uZYjdnemmtpzfCnzlm1o3vblyo2MGVRIUW6YUNDPfhMH8r5dS0VNI1W1zsivipoIQb8P\nv9/HO/PXcOQeztRu0yYPYv7yiq7seitDx09m0WfOdWu+/uoLSoeO2kKLjtsMMzs1r1/+5VxKh3b+\nN/M78tGSCqZPbBmtZVdXN2/7am0NI0tyKcgOEgr4mDq6H58sq2DK8D68s7Cck+59jxdmr2Z5efeN\n7ujIgDGTWDHvQwDWLZ5PvyEjt6tN6cjxfOcnv+WYH9/GQWddRZ9Bw9n7f87uyq630lvOK4AJO+3C\nx++9BYD9Yg4jRo3dQovWmkZyjZswmbsfepKb7/wDl15/K8NGjuaHMy7t9P52ZPbKyuYRNTsNLuSr\nstrmbUvX1zGsbw75WUGCfh+7Diti7soqJg0q5MOlFZzz2Ge8+mUZqzbWM391Nd978CPOf3w21/7r\nC5asr+WuVxZ19LSdrnD4BCoWfAJA1YoF5PYfvoUWHbfJHzyayqXOSNsNCz+hcMTELur1pnrLex1g\nztdV7DPK+ZM7eVABi9a3/C5dVl7H0D7Z5GcFCPp97DK0iM9XVTFxYAEfL9/IBU/M4fUF61lV6RQk\n7zxxJ4w7vezuI/o0F8I6U3DLu4iIiIiIiIiIiKTFYmPM74AXgG8CHxljjgFqN99sxzzz3lIO3mUI\nr/78WADOvvcN/mfaaHKzQzzyX8uVD7/PszceiQ8fj7xiWVNRzz0z5/H7Cw7g5Z8dQyjo54bHPqIh\nEufqR97n/hn7c9YRE6isjXDar1/ryq63Mn6PaSyZ9wmP3nQRAMecfTmfv/Mq0cYGdj3o6Ob9Uqf3\na68NwMHfO4fnH7yDT1+ZSVZOHsfNuKbb4nhp7hqmmRKe+tE+AFzx1zkcu9tgcsMBnnh/BT97Zj5/\nPncv8MGT76+grKqRaCzBpUeNZ8ahY6msj3Ll31pfv6e7p5wDGLnbvnw9/xP+/UunEHLgDy5h0Qev\nE400MGHakS07prwe7bVJt95yXoEzNeHsj9/n6gtOB+CCK29k1isv0tBQz2HHnNCyYwdz+6Vjasz2\nvG7Xs9eovjzw/d0AuPm5Lzl8Un9yQgGemb2au/77FfecPAUf8O/ZayivjRBLJDjnwEmcvt8Iqhpi\n3PKcTW8QQPHEvahYNJvZDzjnwbgTLmDdnFkkoo0M3P3Qlh1Tfu7ttQEYfcSpLHzmdyTjMXJKh1Iy\neZ9ui6O3vNcB3lxYztQRfbn/5CkA3PriQg6dUEp2yM+zc9dyz+uLufPEnfH54Nm5ayivjRKNJ7lp\nvwmcutdwqhtj3Paicz2y21/+ih8fMoZoIsmG2gi//E/nj+j0peOXeydIlpVVb3mvXqq0tACvxu/l\n2MHb8St2b8YO3o7fy7EDlJYW9Iz/HDKHZ/MjvVe8G79i92bs4O34vRw7eDt+r+ZGxpgwcBYwCfgM\neAiYCiy01pZvzTFyTngwIz/8SlX/9JkAPOJOjZipTpvqjLwYdclzae7Jjlvy62O4/fXF6e7GDrt8\n+uiMP6+g5dz6YlWX1sK73KTBzhRve/789bT2ozN8cM10znxiXrq7scMePGmnXvNen/arWenuxg57\n67L9ocMStEZ4iYiIiIiIiIhID2WtjSXppHAAABQESURBVOBcxyvVe+noi4iIiPRsuoaXiIiIiIiI\niIiIiIiIZDQVvERERERERERERERERCSjqeAlIiIiIiIiIiIiIiIiGU0FLxEREREREREREREREclo\nKniJiIiIiIiIiIiIiIhIRlPBS0RERERERERERERERDKaCl4iIiIiIiIiIiIiIiKS0VTwEhERERER\nERERERERkYymgpeIiIiIiIiIiIiIiIhkNBW8REREREREREREREREJKOp4CUiIiIiIiIiIiIiIiIZ\nTQUvERERERERERERERERyWgqeImIiIiIiIiIiIiIiEhGU8FLREREREREREREREREMpoKXiIiIiIi\nIiIiIiIiIpLRVPASERERERERERERERGRjBZMdwcAjDE+4H5gF6ABONNauzi9vRIRERFJH+VHIiIi\nIiIiIiJbr6eM8PoWkGWt3Re4Grgzzf0RERERSTflRyIiIiIiIiIiW6mnFLymAS8CWGvfB/ZIb3dE\nRERE0k75kYiIiIiIiIjIVuoRUxoChUBlynLMGOO31ibS1SERERGRNFN+JCIiItIJ6p8+M91d6DSn\nTR2e7i50iiW/PibdXegUl08fne4udIrecl4BTBqcl+4udIoPrpme7i50igdP2indXegUveW9/tZl\n+6e7C12upxS8qoCClOUtfZjjKy0t2Mzm3s/L8Xs5dvB2/Irdu7wcv5djF+VH28LLsYO341fs3uXl\n+L0cOyh+2S6+dHdAREREul5PmdLwbeBoAGPM3sDc9HZHREREJO2UH4mIiIiIiIiIbKWeMsLraeAw\nY8zb7vLp6eyMiIiISA+g/EhEREREREREZCv5kslkuvsgIiIiIiIiIiIiIiIist16ypSGIiIiIiIi\nIiIiIiIiIttFBS8RERERERERERERERHJaCp4iYiIiIiIiIiIiIiISEYLprsD28IY4wPuB3YBGoAz\nrbWL09urrmOM2Qu4zVp7kDFmDPAIkADmWWtnuPucBZwNRIFbrLXPpau/ncEYEwQeAkYCYeAW4As8\nEDuAMcYPPAAYnHjPBRrxSPwAxpj+wEfAoUAcj8RujPkYqHQXlwA/xyOxAxhjrgKOA0I4v+ffxAPx\nG2N+AJwGJIEcnL9v+wN30ctjh+bf+X/C+Z0fA87CQ+/7zqDcqPefK8qNlBt5NTcCb+dHXs2NwNv5\nkXKjzNPbcrHUXCvdfdle7eVO1tqZae3UdmgvB7LWfpHeXm2/1HzGWrsg3f3ZXm1zE2vtGensz/Zq\nm2dYax9Oc5e2WQf5wkBrbVU6+7Wt2vvbn4nvEWNMGHgYGI3zHplhrV2Ujr5k2givbwFZ1tp9gauB\nO9Pcny5jjLkc5w9blrvqTuAaa+2BgN8Yc7wxZgBwIbAPcCRwqzEmlJYOd55TgPXW2gNwYroX78QO\ncCyQtNZOA67H+afeM/G7v+R/B9S5qzwRuzEmC8Bae7B7OwOPxA5gjDkQ2Mf93T4dGI5H4rfW/sla\ne5C19mDgY+BHwA14IHbX0UDAWrsfcDMe+53XSZQb9f5zRbmRciPP5Ubg7fzIy7kReD4/Um6UeXpN\nLtZOrpWpUnOno3Byp0zUXg6UkdrJZzJSB7lJxmknzxiW3h5tn3byhQszrdjlau9vfyY6C6i21u6D\nk7vdl66OZFrBaxrwIoC19n1gj/R2p0t9BZyQsry7tXaW+/gF4DBgT+Ata23MfUMvBKZ0bzc73ZM4\nf8gBAjiV7W94JHastc/gfEsPYARQgYfiB34F/BZYBfjwTuy7AHnGmJeMMf91v1XnldgBjgDmGWP+\nBfwbeBZvxY8xZg9gkrX2Qbzz+x5gARB0vxlbhPMNZU+99p1AuZGjN58ryo2UG3kxNwJv50eez43A\ns/mRcqPM05tysba5VqZKzZ38OO+jjNMmBxqJkwNlqtR8JpO1l5tkovbyjIyVki/8Md192U5t//ZH\n0tyf7TUJJ0/BHaE2MV0dybSCVyEtw0YBYu4Q317HWvs0zgcaTXwpj6txfhYFtP551OC8MTKWtbbO\nWltrjCkAngKuxSOxN7HWJowxjwC/AR7HI/EbY04D1llrX6Yl5tT3d6+NHedbTrdba48AzgP+gkde\nd1cJsDtwIi3xe+W1b3I1cGM763t77DXAKOBL4Pc4v/e8dO53BuVGjl57rig3Um7k0dwIvJ0fKTdy\neDE/Um6UeXpNLtZOrpWROsidMlJKDnQ3zt+CjNNBPpOpNslNMvT93jbPeDy93dlhVwM3pbsTO6C9\nv/2Z6DPgmwDGmL2BwW4Rr9tl2puyCiexa+K31ibS1ZlulhpnAbAR5+dR2M76jGaMGQa8CvzJWvs3\nPBR7E2vtacB44EGceWib9Ob4TwcOM8a8hvOtmUeB0pTtvTn2BbjJq7V2IVAODEjZ3ptjByfel9xv\nqC7Amfs+9R/2Xh2/MaYIGG+tfdNd5aXfeZcAL1prDS3v+3DK9t4ef2dQbuTo1eeKciPlRngvNwJv\n50eezo3A0/mRcqPM4+VcrMdqkzs9ke7+7IjUHMgYk7OF3Xui1HxmV+BR93pemai93GRQWnu0fTbJ\nM4wxJenu1PZIyRfeSHdfdsAmf/vd62FlmoeAamPMm8DxwMfW2mQ6OpJpBa+3cea1bKoUzk1vd7rV\nJ8aYA9zHRwGzgA+BacaYsPsGnwDMS1cHO4M7F/lLwBXW2j+5qz/1QuwAxphTjHPhSHD+sY0DH7nz\n60Ivjt9ae6A79+5BON8K+D7wgkde+x8CdwAYYwbj/PP6Hy+87q63cK490BR/HvCKh+I/AHglZdkz\nv/OADbR8I3YjEMSJ3yuvfWdQbuToteeKciPlRh7NjcDb+ZHXcyPwbn6k3Cjz9MZcLKNH4XSQO2Wc\nDnKgjCumtpPPnGqtXZfufm2ntrlJAbA6rT3aPm3zjFycIlgmapsvZKL2/vYH0ted7TYVeMU610/8\nO7A4XR0JpuuJt9PTON8KeNtdPj2dnelmlwEPuBeinQ/83VqbNMb8BucXlQ/nQraZOs9nk6uBPsD1\nxpgbgCRwEXCPB2IH+CfwsDHmDZz3549whrQ+6JH42/LKef9HnNd9Fk4CexpOsuGJ191a+5wxZn9j\nzAc4cZ0HLMUj8QOG1omAV857gLuAh9xvAIWAq3AuNuuV174zKDfq/eeKciPlRqm8ct6Dh/Mj5UaA\nd/Mj5UaZpzfmYmn5Rn4nai93Ospa25jebm2ztjnQRRkYQ1uZfm61zU1+mIkjOtvJM85P10icTtA2\nX8hEbf/2X22trU9zn7bHQuBmY8y1ONccPCNdHfElk5l6PouIiIiIiIiIiIiIiIhk3pSGIiIiIiIi\nIiIiIiIiIq2o4CUiIiIiIiIiIiIiIiIZTQUvERERERERERERERERyWgqeImIiIiIiIiIiIiIiEhG\nU8FLREREREREREREREREMpoKXiIiIiIiIiIiIiIiIpLRgunugIj0XsYYA1wLHAT0B6qAD4F7rLUv\nuPv0A+4G/mCtnZWuvoqIiIh0NeVGIiIikk7GmNeBA9qsjgFlwMvANdbaVV38/GFr7b7u8hLgXWvt\n97ayfafmScaYR4AjrLWDNrNPArjNWntNZx43HccS8QKN8BKRLmGMmQR8AAwHLgMOBc4GGoHnjDFn\nu7tOBf4P8KWjnyIiIiLdQbmRiIiI9ABJYB6wF7C3ezsIuBH4JvCaMSbcxc+f6lvAddvQvrPzpGQ7\nfeppx+2qPor0ShrhJSJd5VKgBjjEWhtPWf+0Mea/wM+BP+AkKfrDLSIiIr2dciMRERHpCWqstR+2\nWfe2MaYe+BNwHPD37uiItXb2NjZRniQim6WCl4h0lQHufRCIt9l2PbCPMeYc4Lc4ycrrxpjXrbUH\nAxhjjnT32w2oA54DrrDWrnW3Hwi8hvMNpMtwvpW0DngQuMVam3T3GwncC+wJ5AMWuMta+6cuiFlE\nRESkI8qNREREpCf7CKegNBLAGPOw+/gz4DSgGphkra0xxpyC82WeCcBG4Cmc6RBrmg5mjJkI3AFM\nw5nG+Rdtn9AYsxR4p2lKQ2NMALgaOBUYCqwEHrDW3m6M+QHwMNuRJ7n7DAbuwhllHwd+z3bMfmaM\nGQb81D1Of5wvNL0BXGqtXdJm3+8DPwGG4Pwcr7PWvpKyPeT2+xRgMLAMuN9ae/e29ktEHJrSUES6\nykxgEPChMeZiY8wUY4wPwFr7rrX2TpxvDF3i7n8ecD6AMeZEnORkJfBtd58DcRKavDbP82ecD2qO\nB/6CMwz/dvc4PuB5tx9nAEcBnwAPGWMO6YKYRURERDqi3EhERER6sknu/Vcp6/YDpgDfwSno1Bhj\nfgw8CryLMxrsRpxpBp9rym2MMQOAt3GKOKcCFwMzgH3bPGfb0Vp/wrne6WM4X+J5GLjVGHM98Czb\nmScZY7KBWThf+JkB/BA4GPjfbfj5YIzJwilu7QpcBBzmxn8I8Mc2u/fHycFuwfn51QEvGGP2TNmn\nKff7LXAMTuHwDmPMLdvSLxFpoRFeItIlrLW/N8aU4Hwz5w6cbwlVuRco/aO1dqa1ttwY86XbZL61\ntunxr4DXrbUnNR3PGPMWzoc3M4BfpjzVC9ba89zHLxtjCoALjTE/x/kdNwHnW0Yz3X3eMMaU41wv\nQ0RERKRbKDcSERGRnsIdSdWkCOeaXr/CKXa9kLItAJxlrV3stisAbgIesdaen3K8z4E3ge8CT+IU\nuLKBI1JGo38ALNxMnyYC3wOustY25TavGmP6AwdYa2/egTzpBzij1faw1n7q7vMa0GpE1lYYDywH\nzrHWWnfdm8aYse5zpfIBJ1pr33Kf71VgMU5B73hjzMHAscBp1tpH3TavGGMageuMMfdZa1dtY/9E\nPE8FLxHpMtbaW4wxdwNHANOBA3D+mB9njPmLtfb7bdsYY8bjXMz9120SsBXAbPdYqR/qtJ1+5yng\nAmA/a+1MY8xc4GZjzO7Ai8Bz1torOiVAERERkW2g3EhERER6gL2BaJt1SZwRWedaa1O/BFPbVOxy\n7QPkAv9uk5e8B5Tj5CVP4oyw+ih1SkFr7XJjzLtAuIN+HeD245+pK621l7S/+zblSQcCXzcVu9zj\n1hhjngMO7+j4bVlr5wLTjTE+Y8xoYAzOl4mmAT5jTMha2/SzXd1U7HLbNhhjXsAZ7QXO6LAkMLNN\n3/+NU1Q8BGfkvohsAxW8RKRLufM3/8O9YYwZDtwHfM8Y8xecP+6+lCYl7v2dwK/bHC4JLGizvLLN\nPuvc+37u/WE48yGfgDO0HffC8Oe2nVtZREREpKspNxIREZE0m4tzTS4fTu7QCKy01la1s29Nm+US\nt90/aJ2v4B5rsPu4GJjXzvFWAyM66Fexe7+2g+3t2do8qRgo66A/28QYcxHOiP1SYD3Otblq3c2p\nP5M17TRfBxS4Uz8Wu/uXt7NfEue6XyKyjVTwEpFO514I9APgVmvtfanb3G/0nAWsAiazaQK00b2/\nFvhPO4dvO91OKTA/ZbnpgvDr3OdbB1yIM5XPBJz5pW8A/oDzgY+IiIhIl1JuJCIiIj1IbepIp23U\nlJecTvsFrWr3vgwY2M72knbWtT12acpxMMYMwxlJ9dZm2mwpTyoDdtrG/mzCGHMSTmHteuABN6/C\nGPMLnOudperHpgYCG6y1SWPMRrd/bds10XSGItvBn+4OiEivtAZnePx57VxIHcDgfFtlDhBvs20+\nzrd5xllrP2m6AZ/jDOk+ImVfH863k1OdBNQDs4wxE4wxK40x3wKw1n7pzgP9Mh1/o0hERESksyk3\nEhERkd7gXZwizYg2eclK4Daca4GBk1vsYYwZ1dTQvRbX3ps59iycXOZbbdZfDvwdJ1eK03oU1dbm\nSS8DA40x01L6k0XrPGprHADUWWtvSSl2BVOOk/pZ+whjzOSU5ysAjnH7AvA6zvSOeW363g+4BRi0\njX0TETTCS0S6gLU2YYw5B3gG+NQYcy/OBzh+nG+uXAw8Y6192b1+BMA3jTEbrbVzjDFXAX80xsRw\n5m7OAn6Mkxj9qs3TnW+MqQNew5l3+Wzgene6oC+NMWXAb4wxRTgXI50KHAX8ost+ACIiIiIplBuJ\niIhIb2CtrTDG3AZca4zJxxlVVQxcA4wEfuTuejdwBvCiMeYGIAJcx6bTIKYee64x5m841xrNAt4H\n9gXOBa6y1saNMRXu7tuaJ/3F7dtTxphrcYpkF7t9r9yGH8F7wLnGmN/gTOvYH+daqU2jx/KABvdx\nA/C0MeYaN/5r3L7d5G5/Aafo9ZQx5uc41xybBNwMfE37I+hEZAs0wktEuoS19j/AHsA7wEXAc8C/\ncC7M/hPgRHfXT4EngBnAY27bP+FcxHNnnGTlYSAGHG6tfaPNU10C7I9zUc9vAudba29L2X408CrO\nt2NewkmUbgJu7LRgRURERLZAuZGIiIj0EMkd2ddaexNOnnI4MBO4B+dLNPtba79099mI86WeOcDv\ngN/jFHhmtnP81Of4PvBL4CzgWeB/gRnW2jvd7duVJ1lrY8AhwPM4X/L5C7DI7dfW/AyS7nH+jDMV\n9LFuPL/AuU7Y8e6+B6a0+xy4Hef6Yn/DuR7a/tZa6x4riTPi62Gc4ttLwFVufAdba6Nt+iAiW8GX\nTOr9IiKZxxhzIM6HNUe5HyCJiIiIeJZyIxERERER8TqN8BKRTNbhUHgRERERD1JuJCIiIiIinqWC\nl4hkMg1RFREREWmh3EhERERERDxLUxqKiIiIiIiIiIiIiIhIRtMILxEREREREREREREREcloKniJ\niIiIiIiIiIiIiIhIRlPBS0RERERERERERERERDKaCl4iIiIiIiIiIiIiIiKS0VTwEhERERERERER\nERERkYymgpeIiIiIiIiIiIiIiIhktP8HtU+Fgs9+RT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ffd6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (30,9));\n",
    "fig.subplots_adjust(wspace=0.15, hspace=0) # Bringing subplots closer together. \n",
    "axes[0].plot(step_list, cost_list);\n",
    "axes[0].set_figure\n",
    "axes[0].set_title('Cost vs Steps', size = 20);\n",
    "axes[0].set_xlabel('Steps', size = 17);\n",
    "axes[0].set_ylabel('Cost', size = 17);\n",
    "axes[1].plot(step_list, V_accur_list);\n",
    "axes[1].set_title('Validation Accuracy vs Steps', size = 20);\n",
    "axes[1].set_xlabel('Steps', size = 17);\n",
    "axes[1].set_ylabel('Validation Accuracy', size = 17);\n",
    "\n",
    "# predictions\n",
    "predicted = [np.argmax(predictions[i,:], 0) for i in xrange(0,predictions.shape[0])]\n",
    "#actual\n",
    "t_labels = [np.where(test_labels[i] == 1)[0][0] for i in xrange(0,predictions.shape[0])]\n",
    "\n",
    "cm = metrics.confusion_matrix(t_labels, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True,ax = axes[2], cmap = 'Blues_r', cbar = False);\n",
    "axes[2].set_ylabel('Actual label', size = 17);\n",
    "axes[2].set_xlabel('Predicted label', size = 17);\n",
    "confusion_title = 'Confusion Matrix ({}%)'.format(test_accuracy)\n",
    "axes[2].set_title(confusion_title, size = 20);\n",
    "fig.suptitle('Multinomial Logistic Regression', y=1,x = .52, size = 40);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x68f-hxRGm3H"
   },
   "source": [
    "Let's now switch to stochastic gradient descent training instead, which is much faster.\n",
    "\n",
    "The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `session.run()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "qhPMzWYRGrzM"
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "    # Input data. For the training data, we use a placeholder that will be fed\n",
    "    # at run time with a training minibatch.\n",
    "    tf_train_dataset = tf.placeholder(tf.float32,\n",
    "                                    shape=(batch_size, image_size * image_size))\n",
    "    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "  \n",
    "    # Variables.\n",
    "    weights = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "    biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "    # Training computation.\n",
    "    logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "  \n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "  \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.nn.softmax(logits)\n",
    "    valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XmVZESmtG4JH"
   },
   "source": [
    "Let's run it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 6
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 66292,
     "status": "ok",
     "timestamp": 1449848003013,
     "user": {
      "color": "",
      "displayName": "",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "",
      "photoUrl": "",
      "sessionId": "0",
      "userId": ""
     },
     "user_tz": 480
    },
    "id": "FoF91pknG_YW",
    "outputId": "d255c80e-954d-4183-ca1c-c7333ce91d0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 18.070415\n",
      "Minibatch accuracy: 7.0%\n",
      "Validation accuracy: 9.7%\n",
      "Minibatch loss at step 500: 2.116691\n",
      "Minibatch accuracy: 71.1%\n",
      "Validation accuracy: 75.3%\n",
      "Minibatch loss at step 1000: 1.111737\n",
      "Minibatch accuracy: 76.6%\n",
      "Validation accuracy: 76.6%\n",
      "Minibatch loss at step 1500: 1.283048\n",
      "Minibatch accuracy: 77.3%\n",
      "Validation accuracy: 77.3%\n",
      "Minibatch loss at step 2000: 1.141959\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 76.7%\n",
      "Minibatch loss at step 2500: 1.079272\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 78.6%\n",
      "Minibatch loss at step 3000: 0.915368\n",
      "Minibatch accuracy: 75.0%\n",
      "Validation accuracy: 78.5%\n",
      "Minibatch loss at step 3500: 0.706005\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 78.8%\n",
      "Minibatch loss at step 4000: 0.979438\n",
      "Minibatch accuracy: 75.8%\n",
      "Validation accuracy: 79.5%\n",
      "Minibatch loss at step 4500: 0.726778\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 79.7%\n",
      "Minibatch loss at step 5000: 0.565493\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 79.9%\n",
      "Test accuracy: 87.1%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 5001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print(\"Initialized\")\n",
    "    for step in range(num_steps):\n",
    "        # Pick an offset within the training data, which has been randomized.\n",
    "        # Note: we could use better randomization across epochs.\n",
    "        offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "        # Generate a minibatch.\n",
    "        batch_data = train_dataset[offset:(offset + batch_size), :]\n",
    "        batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "        # Prepare a dictionary telling the session where to feed the minibatch.\n",
    "        # The key of the dictionary is the placeholder node of the graph to be fed,\n",
    "        # and the value is the numpy array to feed to it.\n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        _, l, predictions = session.run(\n",
    "          [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "        if (step % 500 == 0):\n",
    "            print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "            print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "            print(\"Validation accuracy: %.1f%%\" % accuracy(valid_prediction.eval(), valid_labels))\n",
    "    print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7omWxtvLLxik"
   },
   "source": [
    "---\n",
    "Problem\n",
    "-------\n",
    "\n",
    "Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units [nn.relu()](https://www.tensorflow.org/versions/r0.7/api_docs/python/nn.html#relu) and 1024 hidden nodes. This model should improve your validation / test accuracy.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "V_accur_list, step_list, cost_list = [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setting up the Computation graph always comes first\n",
    "\n",
    "batch_size = 128\n",
    "n_hidden_nodes = 1024\n",
    "image_size = 28\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    \n",
    "    # Input data. For the training data, we use a placeholder that will be \n",
    "    # fed at runtime with a training minibatch\n",
    "    tf_train_dataset = tf.placeholder(tf.float32,\n",
    "                                    shape=(batch_size, image_size * image_size))\n",
    "    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "    tf_valid_dataset = tf.constant(valid_dataset)\n",
    "    tf_test_dataset = tf.constant(test_dataset)\n",
    "    \n",
    "    # Variables.\n",
    "    weights_01 = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, n_hidden_nodes]))\n",
    "    weights_12 = tf.Variable(tf.truncated_normal([n_hidden_nodes, num_labels]))\n",
    "    biases_01 = tf.Variable(tf.zeros([n_hidden_nodes]))\n",
    "    biases_12 = tf.Variable(tf.zeros([num_labels]))\n",
    "\n",
    "    # Training computation.\n",
    "    z_01= tf.matmul(tf_train_dataset, weights_01) + biases_01\n",
    "    h1 = tf.nn.relu(z_01)\n",
    "    z_12 = tf.matmul(h1, weights_12) + biases_12\n",
    "    loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(z_12, tf_train_labels))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
    "\n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.nn.softmax(z_12)\n",
    "    valid_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf.nn.relu(tf.matmul(tf_valid_dataset, weights_01) + biases_01), weights_12) + biases_12)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset, weights_01) + biases_01), weights_12) + biases_12) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 356.624695\n",
      "Minibatch accuracy: 13.3%\n",
      "Validation accuracy: 34.0%\n",
      "Minibatch loss at step 100: 36.670090\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 78.7%\n",
      "Minibatch loss at step 200: 43.430908\n",
      "Minibatch accuracy: 74.2%\n",
      "Validation accuracy: 79.9%\n",
      "Minibatch loss at step 300: 28.898214\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 78.5%\n",
      "Minibatch loss at step 400: 18.073429\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 79.3%\n",
      "Minibatch loss at step 500: 15.883502\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 81.3%\n",
      "Minibatch loss at step 600: 6.345594\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 80.8%\n",
      "Minibatch loss at step 700: 7.560060\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 80.3%\n",
      "Minibatch loss at step 800: 6.245599\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 80.0%\n",
      "Minibatch loss at step 900: 9.091313\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 80.4%\n",
      "Minibatch loss at step 1000: 6.600840\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 80.5%\n",
      "Minibatch loss at step 1100: 8.093083\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 80.8%\n",
      "Minibatch loss at step 1200: 3.479997\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 81.3%\n",
      "Minibatch loss at step 1300: 10.386351\n",
      "Minibatch accuracy: 79.7%\n",
      "Validation accuracy: 81.4%\n",
      "Minibatch loss at step 1400: 13.853384\n",
      "Minibatch accuracy: 79.7%\n",
      "Validation accuracy: 79.2%\n",
      "Minibatch loss at step 1500: 7.972363\n",
      "Minibatch accuracy: 76.6%\n",
      "Validation accuracy: 81.3%\n",
      "Minibatch loss at step 1600: 4.750757\n",
      "Minibatch accuracy: 75.8%\n",
      "Validation accuracy: 81.1%\n",
      "Minibatch loss at step 1700: 3.290935\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 1800: 5.907562\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 1900: 8.077087\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 81.4%\n",
      "Minibatch loss at step 2000: 4.336357\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 79.7%\n",
      "Minibatch loss at step 2100: 3.476595\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 81.5%\n",
      "Minibatch loss at step 2200: 12.988012\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 81.3%\n",
      "Minibatch loss at step 2300: 5.074364\n",
      "Minibatch accuracy: 78.9%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 2400: 1.946795\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 82.3%\n",
      "Minibatch loss at step 2500: 6.643175\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 80.1%\n",
      "Minibatch loss at step 2600: 5.557855\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 81.8%\n",
      "Minibatch loss at step 2700: 4.511805\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 2800: 4.328233\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 2900: 3.220878\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 3000: 1.767853\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.5%\n",
      "Minibatch loss at step 3100: 4.493433\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 82.1%\n",
      "Minibatch loss at step 3200: 1.451915\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 82.7%\n",
      "Minibatch loss at step 3300: 1.169044\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 3400: 3.009609\n",
      "Minibatch accuracy: 82.0%\n",
      "Validation accuracy: 82.9%\n",
      "Minibatch loss at step 3500: 2.805847\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 81.8%\n",
      "Minibatch loss at step 3600: 1.114717\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 83.5%\n",
      "Minibatch loss at step 3700: 4.010863\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 3800: 2.923351\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 3900: 1.907300\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.1%\n",
      "Minibatch loss at step 4000: 2.007435\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 81.2%\n",
      "Minibatch loss at step 4100: 2.646996\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 82.0%\n",
      "Minibatch loss at step 4200: 2.143230\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 4300: 6.305302\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 4400: 2.816506\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 82.1%\n",
      "Minibatch loss at step 4500: 1.284968\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 82.9%\n",
      "Minibatch loss at step 4600: 4.485236\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 4700: 2.765956\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 4800: 2.890204\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 82.7%\n",
      "Minibatch loss at step 4900: 0.802375\n",
      "Minibatch accuracy: 92.2%\n",
      "Validation accuracy: 81.6%\n",
      "Minibatch loss at step 5000: 1.700094\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 82.4%\n",
      "Minibatch loss at step 5100: 1.130608\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 5200: 2.017298\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 5300: 1.668789\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 5400: 2.232362\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.1%\n",
      "Minibatch loss at step 5500: 3.562922\n",
      "Minibatch accuracy: 78.1%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 5600: 1.995007\n",
      "Minibatch accuracy: 81.2%\n",
      "Validation accuracy: 82.8%\n",
      "Minibatch loss at step 5700: 0.944479\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 83.4%\n",
      "Minibatch loss at step 5800: 2.968962\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 82.6%\n",
      "Minibatch loss at step 5900: 1.411782\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 6000: 3.279909\n",
      "Minibatch accuracy: 79.7%\n",
      "Validation accuracy: 83.3%\n",
      "Minibatch loss at step 6100: 0.994883\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 83.0%\n",
      "Minibatch loss at step 6200: 4.800710\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 81.9%\n",
      "Minibatch loss at step 6300: 1.309372\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 83.4%\n",
      "Minibatch loss at step 6400: 0.618083\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 6500: 2.533028\n",
      "Minibatch accuracy: 80.5%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 6600: 2.040543\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 83.4%\n",
      "Minibatch loss at step 6700: 0.889861\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.6%\n",
      "Minibatch loss at step 6800: 1.643138\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 6900: 0.995437\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 7000: 2.278801\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 7100: 1.928141\n",
      "Minibatch accuracy: 83.6%\n",
      "Validation accuracy: 83.1%\n",
      "Minibatch loss at step 7200: 2.749542\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 7300: 2.970842\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 7400: 2.144320\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 7500: 0.997264\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 83.4%\n",
      "Minibatch loss at step 7600: 0.909625\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 7700: 1.469707\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 84.2%\n",
      "Minibatch loss at step 7800: 2.253751\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 7900: 1.692418\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 8000: 1.328473\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 8100: 1.846039\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 8200: 0.482026\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 8300: 0.906911\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 8400: 1.250573\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 83.7%\n",
      "Minibatch loss at step 8500: 0.673157\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 8600: 0.670258\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 83.9%\n",
      "Minibatch loss at step 8700: 0.719804\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 8800: 0.702613\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 83.8%\n",
      "Minibatch loss at step 8900: 1.196133\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 9000: 1.579878\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.4%\n",
      "Minibatch loss at step 9100: 0.531257\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 9200: 2.059568\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 84.4%\n",
      "Minibatch loss at step 9300: 1.120453\n",
      "Minibatch accuracy: 92.2%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 9400: 0.839551\n",
      "Minibatch accuracy: 84.4%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 9500: 1.132492\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 84.0%\n",
      "Minibatch loss at step 9600: 0.870472\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 84.4%\n",
      "Minibatch loss at step 9700: 1.365263\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 84.2%\n",
      "Minibatch loss at step 9800: 0.477953\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 9900: 2.443820\n",
      "Minibatch accuracy: 85.9%\n",
      "Validation accuracy: 84.4%\n",
      "Minibatch loss at step 10000: 1.437547\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 10100: 0.689287\n",
      "Minibatch accuracy: 92.2%\n",
      "Validation accuracy: 84.4%\n",
      "Minibatch loss at step 10200: 1.130723\n",
      "Minibatch accuracy: 90.6%\n",
      "Validation accuracy: 84.6%\n",
      "Minibatch loss at step 10300: 1.601733\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 10400: 2.026353\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 10500: 0.976621\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 10600: 0.660402\n",
      "Minibatch accuracy: 93.8%\n",
      "Validation accuracy: 84.4%\n",
      "Minibatch loss at step 10700: 1.380933\n",
      "Minibatch accuracy: 85.2%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 10800: 0.775585\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 10900: 0.729696\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 84.2%\n",
      "Minibatch loss at step 11000: 1.937350\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 83.5%\n",
      "Minibatch loss at step 11100: 0.866588\n",
      "Minibatch accuracy: 88.3%\n",
      "Validation accuracy: 84.1%\n",
      "Minibatch loss at step 11200: 0.652418\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 84.6%\n",
      "Minibatch loss at step 11300: 0.561855\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.5%\n",
      "Minibatch loss at step 11400: 0.845003\n",
      "Minibatch accuracy: 92.2%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 11500: 0.961933\n",
      "Minibatch accuracy: 82.8%\n",
      "Validation accuracy: 85.2%\n",
      "Minibatch loss at step 11600: 1.076634\n",
      "Minibatch accuracy: 89.1%\n",
      "Validation accuracy: 84.8%\n",
      "Minibatch loss at step 11700: 0.439123\n",
      "Minibatch accuracy: 91.4%\n",
      "Validation accuracy: 84.8%\n",
      "Minibatch loss at step 11800: 1.357126\n",
      "Minibatch accuracy: 87.5%\n",
      "Validation accuracy: 84.3%\n",
      "Minibatch loss at step 11900: 1.154935\n",
      "Minibatch accuracy: 89.8%\n",
      "Validation accuracy: 83.2%\n",
      "Minibatch loss at step 12000: 2.236250\n",
      "Minibatch accuracy: 86.7%\n",
      "Validation accuracy: 84.6%\n",
      "Test accuracy: 91.1%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 12001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print(\"Initialized\")\n",
    "    for step in range(num_steps):\n",
    "        # Pick an offset within the training data, which has been randomized.\n",
    "        # Note: we could use better randomization across epochs.\n",
    "        offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
    "        # Generate a minibatch.\n",
    "        batch_data = train_dataset[offset:(offset + batch_size), :]\n",
    "        batch_labels = train_labels[offset:(offset + batch_size), :]\n",
    "        # Prepare a dictionary telling the session where to feed the minibatch.\n",
    "        # The key of the dictionary is the placeholder node of the graph to be fed,\n",
    "        # and the value is the numpy array to feed to it.\n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        _, l, predictions = session.run(\n",
    "          [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "        if (step % 100 == 0):\n",
    "            cost_list.append(l)\n",
    "            step_list.append(step)\n",
    "            print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "            print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "            valid_accuracy = accuracy(valid_prediction.eval(), valid_labels)\n",
    "            V_accur_list.append(valid_accuracy)\n",
    "            print(\"Validation accuracy: %.1f%%\" % valid_accuracy)\n",
    "    test_accuracy = accuracy(test_prediction.eval(), test_labels)\n",
    "    print('Test accuracy: %.1f%%' % test_accuracy)\n",
    "    #This line below allows for taking the predictions to a pandas dataframe and eventually a heatmap/confusion matrix\n",
    "    pred = test_prediction.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hH5dKffwAyHv51QuJmIVXrQDX5q95yme2E9VxHA7sVFSA5XH3uwlFVDb+ExOK6GZUgE3d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3jaDU1IxyitR+EhLi6c8Ao08Di/4MPPo0sOjPwKOoCAAAcHKWLYIZLIcIAACsZaikmaZpVpC0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\nZDlEAAAAyWB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BRp+xXAT7FfCgc+4p//luYKZzbuAVbI8u9MTYePwP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\nRERERERk3Pn/o/xwapFlh3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x181c17d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (30,9));\n",
    "fig.subplots_adjust(wspace=0.15, hspace=0) # Bringing subplots closer together. \n",
    "axes[0].plot(step_list, cost_list);\n",
    "axes[0].set_figure\n",
    "axes[0].set_title('Cost vs Steps', size = 20);\n",
    "axes[0].set_xlabel('Steps', size = 17);\n",
    "axes[0].set_ylabel('Cost', size = 17);\n",
    "axes[1].plot(step_list, V_accur_list);\n",
    "axes[1].set_title('Validation Accuracy vs Steps', size = 20);\n",
    "axes[1].set_xlabel('Steps', size = 17);\n",
    "axes[1].set_ylabel('Validation Accuracy', size = 17);\n",
    "\n",
    "# predictions\n",
    "predicted = [np.argmax(pred[i,:], 0) for i in xrange(0,pred.shape[0])]\n",
    "#actual\n",
    "t_labels = [np.where(test_labels[i] == 1)[0][0] for i in xrange(0,pred.shape[0])]\n",
    "\n",
    "cm = metrics.confusion_matrix(t_labels, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True,ax = axes[2], cmap = 'Blues_r', cbar = False);\n",
    "axes[2].set_ylabel('Actual label', size = 17);\n",
    "axes[2].set_xlabel('Predicted label', size = 17);\n",
    "confusion_title = 'Confusion Matrix ({}%)'.format(test_accuracy)\n",
    "axes[2].set_title(confusion_title, size = 20);\n",
    "fig.suptitle('1-Hidden Layer Neural Network', y=1,x = .52, size = 40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "2_fullyconnected.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
